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400 |
Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised Data
|
[
"Lilin Zhang",
"Chengpei Wu",
"Ning Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Weakly_Supervised_Contrastive_Adversarial_Training_for_Learning_Robust_Features_from_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Weakly_Supervised_Contrastive_Adversarial_Training_for_Learning_Robust_Features_from_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Weakly_Supervised_Contrastive_CVPR_2025_supplemental.pdf
|
2503.11032
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Lilin and Wu, Chengpei and Yang, Ning},
title = {Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised Data},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25718-25727}
}
|
Existing adversarial training (AT) methods often suffer from incomplete perturbation, meaning that not all non-robust features are perturbed when generating adversarial examples (AEs). This results in residual correlations between non-robust features and labels, leading to suboptimal learning of robust features. However, achieving complete perturbation--perturbing as many non-robust features as possible--is challenging due to the difficulty in distinguishing robust and non-robust features and the sparsity of labeled data. To address these challenges, we propose a novel approach called Weakly Supervised Contrastive Adversarial Training (WSCAT). WSCAT ensures complete perturbation for improved learning of robust features by disrupting correlations between non-robust features and labels through complete AE generation over partially labeled data, grounded in information theory. Extensive theoretical analysis and comprehensive experiments on widely adopted benchmarks validate the superiority of WSCAT. Our code is available at https://github.com/zhang-lilin/WSCAT.
|
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] |
401 |
From Poses to Identity: Training-Free Person Re-Identification via Feature Centralization
|
[
"Chao Yuan",
"Guiwei Zhang",
"Changxiao Ma",
"Tianyi Zhang",
"Guanglin Niu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_From_Poses_to_Identity_Training-Free_Person_Re-Identification_via_Feature_Centralization_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_From_Poses_to_Identity_Training-Free_Person_Re-Identification_via_Feature_Centralization_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_From_Poses_to_CVPR_2025_supplemental.pdf
|
2503.00938
|
@InProceedings{Yuan_2025_CVPR,
author = {Yuan, Chao and Zhang, Guiwei and Ma, Changxiao and Zhang, Tianyi and Niu, Guanglin},
title = {From Poses to Identity: Training-Free Person Re-Identification via Feature Centralization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24409-24418}
}
|
Person re-identification (ReID) aims to extract accurate identity representation features. However, during feature extraction, individual samples are inevitably affected by noise (background, occlusions, and model limitations). Considering that features from the same identity follow a normal distribution around identity centers after training, we propose a Training-Free Feature Centralization ReID framework (Pose2ID) by aggregating the same identity features to reduce individual noise and enhance the stability of identity representation, which preserves the feature's original distribution for following strategies such as re-ranking. Specifically, to obtain samples of the same identity, we introduce two components: Identity-Guided Pedestrian Generation: by leveraging identity features to guide the generation process, we obtain high-quality images with diverse poses, ensuring identity consistency even in complex scenarios such as infrared, and occlusion. Neighbor Feature Centralization: it explores each sample's potential positive samples from its neighborhood. Experiments demonstrate that our generative model exhibits strong generalization capabilities and maintains high identity consistency. With the Feature Centralization framework, we achieve impressive performance even with an ImageNet pre-trained model without ReID training, reaching mAP/Rank-1 of 52.81/78.92 on Market1501. Moreover, our method sets new state-of-the-art results across standard, cross-modality, and occluded ReID tasks, showcasing strong adaptability.
|
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] |
402 |
ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration
|
[
"Johan Edstedt",
"André Mateus",
"Alberto Jaenal"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Edstedt_ColabSfM_Collaborative_Structure-from-Motion_by_Point_Cloud_Registration_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Edstedt_ColabSfM_Collaborative_Structure-from-Motion_by_Point_Cloud_Registration_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Edstedt_ColabSfM_Collaborative_Structure-from-Motion_CVPR_2025_supplemental.pdf
|
2503.17093
|
@InProceedings{Edstedt_2025_CVPR,
author = {Edstedt, Johan and Mateus, Andr\'e and Jaenal, Alberto},
title = {ColabSfM: Collaborative Structure-from-Motion by Point Cloud Registration},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6573-6583}
}
|
Structure-from-Motion (SfM) is the task of estimating 3D structure and camera poses from images. We define Collaborative SfM (ColabSfM) as sharing distributed SfM reconstructions. Sharing maps requires estimating a joint reference frame, which is typically referred to as registration. However, there is a lack of scalable methods and training datasets for registering SfM reconstructions. In this paper, we tackle this challenge by proposing the scalable task of point cloud registration for SfM reconstructions. We find that current registration methods cannot register SfM point clouds when trained on existing datasets. To this end, we propose a SfM registration dataset generation pipeline, leveraging partial reconstructions from synthetically generated camera trajectories for each scene. Finally, we propose a simple but impactful neural refiner on top of the SotA registration method RoITr that yields significant improvements, which we call RefineRoITr. Our extensive experimental evaluation shows that our proposed pipeline and model enables ColabSfM. Code is available at https://github.com/EricssonResearch/ColabSfM.
|
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] |
403 |
RoadSocial: A Diverse VideoQA Dataset and Benchmark for Road Event Understanding from Social Video Narratives
|
[
"Chirag Parikh",
"Deepti Rawat",
"Rakshitha R. T.",
"Tathagata Ghosh",
"Ravi Kiran Sarvadevabhatla"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Parikh_RoadSocial_A_Diverse_VideoQA_Dataset_and_Benchmark_for_Road_Event_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Parikh_RoadSocial_A_Diverse_VideoQA_Dataset_and_Benchmark_for_Road_Event_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Parikh_RoadSocial_A_Diverse_CVPR_2025_supplemental.pdf
|
2503.21459
|
@InProceedings{Parikh_2025_CVPR,
author = {Parikh, Chirag and Rawat, Deepti and T., Rakshitha R. and Ghosh, Tathagata and Sarvadevabhatla, Ravi Kiran},
title = {RoadSocial: A Diverse VideoQA Dataset and Benchmark for Road Event Understanding from Social Video Narratives},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19002-19011}
}
|
We introduce RoadSocial, a large-scale, diverse VideoQA dataset tailored for generic road event understanding from social media narratives. Unlike existing datasets limited by regional bias, viewpoint bias and expert-driven annotations, RoadSocial captures the global complexity of road events with varied geographies, camera viewpoints (CCTV, handheld, drones) and rich social discourse. Our scalable semi-automatic annotation framework leverages Text LLMs and Video LLMs to generate comprehensive question-answer pairs across 12 challenging QA tasks, pushing the boundaries of road event understanding. RoadSocial is derived from social media videos spanning 14M frames and 414K social comments, resulting in a dataset with 13.2K videos, 674 tags and 260K high-quality QA pairs. We evaluate 18 Video LLMs (open-source and proprietary, driving-specific and general-purpose) on our road event understanding benchmark. We also demonstrate RoadSocial's utility in improving road event understanding capabilities of general-purpose Video LLMs.
|
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] |
404 |
MangaNinja: Line Art Colorization with Precise Reference Following
|
[
"Zhiheng Liu",
"Ka Leong Cheng",
"Xi Chen",
"Jie Xiao",
"Hao Ouyang",
"Kai Zhu",
"Yu Liu",
"Yujun Shen",
"Qifeng Chen",
"Ping Luo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MangaNinja_Line_Art_Colorization_with_Precise_Reference_Following_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MangaNinja_Line_Art_Colorization_with_Precise_Reference_Following_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MangaNinja_Line_Art_CVPR_2025_supplemental.zip
|
2501.08332
|
@InProceedings{Liu_2025_CVPR,
author = {Liu, Zhiheng and Cheng, Ka Leong and Chen, Xi and Xiao, Jie and Ouyang, Hao and Zhu, Kai and Liu, Yu and Shen, Yujun and Chen, Qifeng and Luo, Ping},
title = {MangaNinja: Line Art Colorization with Precise Reference Following},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5666-5677}
}
|
Derived from diffusion models, MangaNinja specializes in the task of reference-guided line art colorization. We incorporate two thoughtful designs to ensure precise character detail transcription, including a patch shuffling module to facilitate correspondence learning between the reference color image and the target line art, and a point-driven control scheme to enable fine-grained color matching. Experiments on a self-collected benchmark demonstrate the superiority of our model over current solutions in terms of precise colorization. We further showcase the potential of the proposed interactive point control in handling challenging cases (*e.g.*, extreme poses and shadows), cross-character colorization, multi-reference harmonization, *etc.*, beyond the reach of existing algorithms.
|
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] |
405 |
Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction
|
[
"Cecilia Curreli",
"Dominik Muhle",
"Abhishek Saroha",
"Zhenzhang Ye",
"Riccardo Marin",
"Daniel Cremers"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Curreli_Nonisotropic_Gaussian_Diffusion_for_Realistic_3D_Human_Motion_Prediction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Curreli_Nonisotropic_Gaussian_Diffusion_for_Realistic_3D_Human_Motion_Prediction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Curreli_Nonisotropic_Gaussian_Diffusion_CVPR_2025_supplemental.pdf
|
2501.06035
|
@InProceedings{Curreli_2025_CVPR,
author = {Curreli, Cecilia and Muhle, Dominik and Saroha, Abhishek and Ye, Zhenzhang and Marin, Riccardo and Cremers, Daniel},
title = {Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1871-1882}
}
|
Probabilistic human motion prediction aims to forecast multiple possible future movements from past observations. While current approaches report high diversity and realism, they often generate motions with undetected limb stretching and jitter. To address this, we introduce SkeletonDiffusion, a latent diffusion model that embeds an explicit inductive bias on the human body within its architecture and training. We present a nonisotropic Gaussian diffusion formulation that aligns with the natural kinematic structure of the human skeleton and models relationships between body parts. Results show that our approach outperforms isotropic alternatives, consistently generating realistic predictions while avoiding artifacts such as limb distortion. Additionally, we identify a limitation in commonly used diversity metrics, which may favor models that produce inconsistent limb lengths within the same sequence. SkeletonDiffusion sets a new benchmark on three real-world datasets, outperforming various baselines across multiple evaluation metrics. We release the code on our \href https://ceveloper.github.io/publications/skeletondiffusion/ project page .
|
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] |
406 |
Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation
|
[
"Yiping Wang",
"Xuehai He",
"Kuan Wang",
"Luyao Ma",
"Jianwei Yang",
"Shuohang Wang",
"Simon Shaolei Du",
"Yelong Shen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Is_Your_World_Simulator_a_Good_Story_Presenter_A_Consecutive_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Is_Your_World_Simulator_a_Good_Story_Presenter_A_Consecutive_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Is_Your_World_CVPR_2025_supplemental.pdf
|
2412.16211
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Yiping and He, Xuehai and Wang, Kuan and Ma, Luyao and Yang, Jianwei and Wang, Shuohang and Du, Simon Shaolei and Shen, Yelong},
title = {Is Your World Simulator a Good Story Presenter? A Consecutive Events-Based Benchmark for Future Long Video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13629-13638}
}
|
The current state-of-the-art video generative models can produce commercial-grade videos with highly realistic details. However, they still struggle to coherently present multiple sequential events in specific short stories, which is foreseeable an essential capability for future long video generation scenarios. For example, top T2V generative models still fail to generate a video of the short simple story "how to put an elephant into a refrigerator." While existing detail-oriented benchmarks primarily focus on fine-grained metrics like aesthetic quality and spatial-temporal consistency, they fall short of evaluating models' abilities to handle event-level story presentation. To address this gap, we introduce StoryEval, a story-oriented benchmark specifically designed to assess text-to-video (T2V) models' story-completion capabilities. StoryEval features 423 prompts spanning 7 classes, each representing short stories composed of 2-4 consecutive events. We employ Vision-Language Models, such as GPT-4o and LLaVA-OV-Chat-72B, to verify the completion of each event in the generated videos, applying a unanimous voting method to enhance reliability. Our methods ensure high alignment with human evaluations, and the evaluation of 11 models reveals its challenge, with none exceeding an average story-completion rate of 50%. StoryEval provides a new benchmark for advancing T2V models and highlights the challenges and opportunities in developing next-generation solutions for coherent story-driven video generation. Project website is available at https://ypwang61.github.io/project/StoryEval.
|
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] |
407 |
LookCloser: Frequency-aware Radiance Field for Tiny-Detail Scene
|
[
"Xiaoyu Zhang",
"Weihong Pan",
"Chong Bao",
"Xiyu Zhang",
"Xiaojun Xiang",
"Hanqing Jiang",
"Hujun Bao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_LookCloser_Frequency-aware_Radiance_Field_for_Tiny-Detail_Scene_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_LookCloser_Frequency-aware_Radiance_Field_for_Tiny-Detail_Scene_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_LookCloser_Frequency-aware_Radiance_CVPR_2025_supplemental.zip
|
2503.18513
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Xiaoyu and Pan, Weihong and Bao, Chong and Zhang, Xiyu and Xiang, Xiaojun and Jiang, Hanqing and Bao, Hujun},
title = {LookCloser: Frequency-aware Radiance Field for Tiny-Detail Scene},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16122-16132}
}
|
Humans perceive and comprehend their surroundings through information spanning multiple frequencies. In immersive scenes, people naturally scan their environment to grasp its overall structure while examining fine details of objects that capture their attention. However, current NeRF frameworks primarily focus on modeling either high-frequency local views or the broad structure of scenes with low-frequency information, limited to balance both. We introduce FA-NeRF, a novel frequency-aware framework for view synthesis that simultaneously captures the overall scene structure and high-definition details within a single NeRF model. To achieve this, we propose a 3D frequency quantification method that analyzes the scene's frequency distribution, enabling frequency-aware rendering. Our framework incorporates a frequency grid for fast convergence and querying, a frequency-aware feature re-weighting strategy to balance features across different frequency contents. Extensive experiments show that our method significantly outperforms existing approaches in modeling entire scenes while preserving fine details.
|
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] |
408 |
PICO: Reconstructing 3D People In Contact with Objects
|
[
"Alpár Cseke",
"Shashank Tripathi",
"Sai Kumar Dwivedi",
"Arjun S. Lakshmipathy",
"Agniv Chatterjee",
"Michael J. Black",
"Dimitrios Tzionas"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cseke_PICO_Reconstructing_3D_People_In_Contact_with_Objects_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cseke_PICO_Reconstructing_3D_People_In_Contact_with_Objects_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cseke_PICO_Reconstructing_3D_CVPR_2025_supplemental.pdf
|
2504.17695
|
@InProceedings{Cseke_2025_CVPR,
author = {Cseke, Alp\'ar and Tripathi, Shashank and Dwivedi, Sai Kumar and Lakshmipathy, Arjun S. and Chatterjee, Agniv and Black, Michael J. and Tzionas, Dimitrios},
title = {PICO: Reconstructing 3D People In Contact with Objects},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1783-1794}
}
|
Recovering 3D Human-Object Interaction (HOI) from single color images is challenging due to depth ambiguities, occlusions, and the huge variation in object shape and appearance. Thus, past work requires controlled settings such as known object shapes and contacts, and tackles only limited object classes. Instead, we need methods that generalize to natural images and novel object classes. We tackle this in two main ways:(1) We collect PICO-db, a new dataset of natural images uniquely paired with dense 3D contact correspondences on both body and object meshes. To this end, we use images from the recent DAMON dataset that are paired with annotated contacts, but only on a canonical 3D body. In contrast, we seek contact labels on both the body and the object. To infer these given an image, we retrieve an appropriate 3D object mesh from a database by leveraging vision foundation models. Then, we project DAMON's body contact patches onto the object via a novel method needing only 2 clicks per patch. This minimal human input establishes rich contact correspondences between bodies and objects. (2) We exploit our new dataset in a novel render-and-compare fitting method, called PICO-fit, to recover 3D body and object meshes in interaction. PICO-fit infers contact for the SMPL-X body, retrieves a likely 3D object mesh and contact from PICO-db for that object, and uses the contact to iteratively fit the 3D body and object meshes to image evidence via optimization. Uniquely, PICO-fit works well for many object categories that no existing method can tackle. This is crucial for scaling HOI understanding in the wild. Our data and code are available at https://pico.is.tue.mpg.de.
|
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] |
409 |
Convex Relaxation for Robust Vanishing Point Estimation in Manhattan World
|
[
"Bangyan Liao",
"Zhenjun Zhao",
"Haoang Li",
"Yi Zhou",
"Yingping Zeng",
"Hao Li",
"Peidong Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_Convex_Relaxation_for_Robust_Vanishing_Point_Estimation_in_Manhattan_World_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Convex_Relaxation_for_Robust_Vanishing_Point_Estimation_in_Manhattan_World_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_Convex_Relaxation_for_CVPR_2025_supplemental.pdf
|
2505.04788
|
@InProceedings{Liao_2025_CVPR,
author = {Liao, Bangyan and Zhao, Zhenjun and Li, Haoang and Zhou, Yi and Zeng, Yingping and Li, Hao and Liu, Peidong},
title = {Convex Relaxation for Robust Vanishing Point Estimation in Manhattan World},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15823-15832}
}
|
Determining the vanishing points (VPs) in a Manhattan world, as a fundamental task in many 3D vision applications, consists of jointly inferring the line-VP association and locating each VP. Existing methods are, however, either sub-optimal solvers or pursuing global optimality at a significant cost of computing time. In contrast to prior works, we introduce convex relaxation techniques to solve this task for the first time. Specifically, we employ a "soft" association scheme, realized via a truncated multi-selection error, that allows for joint estimation of VPs' locations and line-VP associations. This approach leads to a primal problem that can be reformulated into a quadratically constrained quadratic programming (QCQP) problem, which is then relaxed into a convex semidefinite programming (SDP) problem. To solve this SDP problem efficiently, we present a globally optimal outlier-robust iterative solver (called GlobustVP), which independently searches for one VP and its associated lines in each iteration, treating other lines as outliers. After each independent update of all VPs, the mutual orthogonality between the three VPs in a Manhattan world is reinforced via local refinement. Extensive experiments on both synthetic and real-world data demonstrate that GlobustVP achieves a favorable balance between efficiency, robustness, and global optimality compared to previous works. The code is publicly available at github.com/wu-cvgl/GlobustVP.
|
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] |
410 |
Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition
|
[
"Yifei Zhang",
"Chang Liu",
"Jin Wei",
"Xiaomeng Yang",
"Yu Zhou",
"Can Ma",
"Xiangyang Ji"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Linguistics-aware_Masked_Image_Modeling_for_Self-supervised_Scene_Text_Recognition_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Linguistics-aware_Masked_Image_Modeling_for_Self-supervised_Scene_Text_Recognition_CVPR_2025_paper.pdf
| null |
2503.18746
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Yifei and Liu, Chang and Wei, Jin and Yang, Xiaomeng and Zhou, Yu and Ma, Can and Ji, Xiangyang},
title = {Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9318-9328}
}
|
Text images are unique in their dual nature, encompassing both visual and linguistic information. The visual component encompasses structural and appearance-based features, while the linguistic dimension incorporates contextual and semantic elements. In scenarios with degraded visual quality, linguistic patterns serve as crucial supplements for comprehension, highlighting the necessity of integrating both aspects for robust scene text recognition (STR). Contemporary STR approaches often use language models or semantic reasoning modules to capture linguistic features, typically requiring large-scale annotated datasets. Self-supervised learning, which lacks annotations, presents challenges in disentangling linguistic features related to the global context. Typically, sequence contrastive learning emphasizes the alignment of local features, while masked image modeling (MIM) tends to exploit local structures to reconstruct visual patterns, resulting in limited linguistic knowledge. In this paper, we propose a Linguistics-aware Masked Image Modeling (LMIM) approach, which channels the linguistic information into the decoding process of MIM through a separate branch. Specifically, we design a linguistics alignment module to extract vision-independent features as linguistic guidance using inputs with different visual appearances. As features extend beyond mere visual structures, LMIM must consider the global context to achieve reconstruction. Extensive experiments on various benchmarks quantitatively demonstrate our state-of-the-art performance, and attention visualizations qualitatively show the simultaneous capture of both visual and linguistic information. The code is available at https://github.com/zhangyifei01/LMIM.
|
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] |
411 |
FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting
|
[
"Fangyu Wu",
"Yuhao Chen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_FruitNinja_3D_Object_Interior_Texture_Generation_with_Gaussian_Splatting_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_FruitNinja_3D_Object_Interior_Texture_Generation_with_Gaussian_Splatting_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_FruitNinja_3D_Object_CVPR_2025_supplemental.pdf
|
2411.12089
|
@InProceedings{Wu_2025_CVPR,
author = {Wu, Fangyu and Chen, Yuhao},
title = {FruitNinja: 3D Object Interior Texture Generation with Gaussian Splatting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11051-11060}
}
|
In the real world, objects reveal internal textures when sliced or cut, yet this behavior is not well-studied in 3D generation tasks today. For example, slicing a virtual 3D watermelon should reveal flesh and seeds. Given that no available dataset captures an object's full internal structure and collecting data from all slices is impractical, generative methods become the obvious approach. However, current 3D generation and inpainting methods often focus on visible appearance and overlook internal textures. To bridge this gap, we introduce FruitNinja, the first method to generate internal textures for 3D objects undergoing geometric and topological changes. Our approach produces objects via 3D Gaussian Splatting (3DGS) with both surface and interior textures synthesized, enabling real-time slicing and rendering without additional optimization. FruitNinja leverages a pre-trained diffusion model to progressively inpaint cross-sectional views and applies voxel-grid-based smoothing to achieve cohesive textures throughout the object. Our OpaqueAtom GS strategy overcomes 3DGS limitations by employing densely distributed opaque Gaussians, avoiding biases toward larger particles that destabilize training and sharp color transitions for fine-grained textures. Experimental results show that FruitNinja substantially outperforms existing approaches, showcasing unmatched visual quality in real-time rendered internal views across arbitrary geometry manipulations. Project page: https://fanguw.github.io/FruitNinja3D.
|
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] |
412 |
Scaling up Image Segmentation across Data and Tasks
|
[
"Pei Wang",
"Zhaowei Cai",
"Hao Yang",
"Ashwin Swaminathan",
"R. Manmatha",
"Stefano Soatto"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Scaling_up_Image_Segmentation_across_Data_and_Tasks_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Scaling_up_Image_Segmentation_across_Data_and_Tasks_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Scaling_up_Image_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Pei and Cai, Zhaowei and Yang, Hao and Swaminathan, Ashwin and Manmatha, R. and Soatto, Stefano},
title = {Scaling up Image Segmentation across Data and Tasks},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4573-4583}
}
|
Traditional segmentation models, while effective in isolated tasks, often fail to generalize to more complex and open-ended segmentation problems, such as free-form, open-vocabulary, and in-the-wild scenarios. To bridge this gap, we propose to scale up image segmentation across diverse datasets and tasks such that the knowledge across different tasks and datasets can be integrated while improving the generalization ability. QueryMeldNet, a novel segmentation framework, is introduced and designed to scale seamlessly across both data size and task diversity. It is built upon a dynamic object query mechanism called query meld, which fuses different types of queries using cross-attention. This hybrid approach enables the model to balance between instance- and stuff-level segmentation, providing enhanced scalability for handling diverse object types. We further enhance scalability by leveraging synthetic data-generating segmentation masks and captions for pixel-level and open-vocabulary tasks-drastically reducing the need for costly human annotations. By training on multiple datasets and tasks at scale, QueryMeldNet continuously improves performance as the volume and diversity of data and tasks increase. It exhibits strong generalization capabilities, boosting performance in open-set segmentation tasks SeginW by 7 points. These advancements mark a key step toward universal, scalable segmentation models capable of addressing the demands of real-world applications.
|
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] |
413 |
Take the Bull by the Horns: Learning to Segment Hard Samples
|
[
"Yuan Guo",
"Jingyu Kong",
"Yu Wang",
"Yuping Duan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Take_the_Bull_by_the_Horns_Learning_to_Segment_Hard_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Take_the_Bull_by_the_Horns_Learning_to_Segment_Hard_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Take_the_Bull_CVPR_2025_supplemental.zip
| null |
@InProceedings{Guo_2025_CVPR,
author = {Guo, Yuan and Kong, Jingyu and Wang, Yu and Duan, Yuping},
title = {Take the Bull by the Horns: Learning to Segment Hard Samples},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15642-15652}
}
|
Medical image segmentation is vital for clinical applications, with hard samples playing a key role in segmentation accuracy. We propose an effective image segmentation framework that includes mechanisms for identifying and segmenting hard samples. It derives a novel image segmentation paradigm: 1) Learning to identify hard samples: automatically selecting inherent hard samples from different datasets, and 2) Learning to segment hard samples: achieving the segmentation of hard samples through effective feature augmentation on dedicated networks. We name our method `Learning to Segment hard samples' (L2S). The hard sample identification module comprises a backbone model and a classifier, which dynamically uncovers inherent dataset patterns. The hard sample segmentation module utilizes the diffusion process for feature augmentation and incorporates a more sophisticated segmentation network to achieve precise segmentation. We justify our motivation through solid theoretical analysis and extensive experiments. Evaluations across various modalities show that our L2S outperforms other SOTA methods, particularly by substantially improving the segmentation accuracy of hard samples. On ISIC dataset, our L2S improves the Dice score on hard samples and overall segmentation by 8.97% and 1.01%, respectively, compared to SOTA methods. The code is available at \href https://github.com/TqlYuanGie/L2S https://github.com/TqlYuanGie/L2S.
|
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414 |
MIMO: A Medical Vision Language Model with Visual Referring Multimodal Input and Pixel Grounding Multimodal Output
|
[
"Yanyuan Chen",
"Dexuan Xu",
"Yu Huang",
"Songkun Zhan",
"Hanpin Wang",
"Dongxue Chen",
"Xueping Wang",
"Meikang Qiu",
"Hang Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_MIMO_A_Medical_Vision_Language_Model_with_Visual_Referring_Multimodal_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_MIMO_A_Medical_Vision_Language_Model_with_Visual_Referring_Multimodal_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_MIMO_A_Medical_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Chen_2025_CVPR,
author = {Chen, Yanyuan and Xu, Dexuan and Huang, Yu and Zhan, Songkun and Wang, Hanpin and Chen, Dongxue and Wang, Xueping and Qiu, Meikang and Li, Hang},
title = {MIMO: A Medical Vision Language Model with Visual Referring Multimodal Input and Pixel Grounding Multimodal Output},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24732-24741}
}
|
Currently, medical vision language models are widely used in medical vision question answering tasks. However, existing models are confronted with two issues: for input, the model only relies on text instructions and lacks direct understanding of visual clues in the image; for output, the model only gives text answers and lacks connection with key areas in the image. To address these issues, we propose a unified medical vision language model MIMO, with visual referring Multimodal Input and pixel grounding Multimodal Output. MIMO can not only combine visual clues and textual instructions to understand complex medical images and semantics, but can also ground medical terminologies in textual output within the image. To overcome the scarcity of relevant data in the medical field, we propose MIMOSeg, a comprehensive medical multimodal dataset including 895K samples. MIMOSeg is constructed from four different perspectives, covering basic instruction following and complex question answering with multimodal input and multimodal output. We conduct experiments on several downstream medical multimodal tasks. Extensive experimental results verify that MIMO can uniquely combine visual referring and pixel grounding capabilities, which are not available in previous models. Our project can be found in https://github.com/pkusixspace/MIMO.
|
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] |
415 |
Bias for Action: Video Implicit Neural Representations with Bias Modulation
|
[
"Alper Kayabasi",
"Anil Kumar Vadathya",
"Guha Balakrishnan",
"Vishwanath Saragadam"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kayabasi_Bias_for_Action_Video_Implicit_Neural_Representations_with_Bias_Modulation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kayabasi_Bias_for_Action_Video_Implicit_Neural_Representations_with_Bias_Modulation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kayabasi_Bias_for_Action_CVPR_2025_supplemental.pdf
|
2501.09277
|
@InProceedings{Kayabasi_2025_CVPR,
author = {Kayabasi, Alper and Vadathya, Anil Kumar and Balakrishnan, Guha and Saragadam, Vishwanath},
title = {Bias for Action: Video Implicit Neural Representations with Bias Modulation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27999-28008}
}
|
We propose a new continuous video modeling framework based on implicit neural representations (INRs) called ActINR. At the core of our approach is the observation that INRs can be considered as a learnable dictionary, with the shapes of the basis functions governed by the weights of the INR, and their locations governed by the biases. Given compact non-linear activation functions, we hypothesize that an INR's biases are suitable to capture motion across images, and facilitate compact representations for video sequences. Using these observations, we design ActINR to share INR weights across frames of a video sequence, while using unique biases for each frame. We further model the biases as the output of a separate INR conditioned on time index to promote smoothness. By training the video INR and this bias INR together, we demonstrate unique capabilities, including 10x video slow motion, 4x spatial super resolution along with 2x slow motion, denoising, and video inpainting. ActINR performs remarkably well across numerous video processing tasks (often achieving more than 6dB improvement), setting a new standard for continuous modeling of videos.
|
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] |
416 |
Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning
|
[
"Bozhou Zhang",
"Nan Song",
"Xin Jin",
"Li Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Bridging_Past_and_Future_End-to-End_Autonomous_Driving_with_Historical_Prediction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Bridging_Past_and_Future_End-to-End_Autonomous_Driving_with_Historical_Prediction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Bridging_Past_and_CVPR_2025_supplemental.pdf
|
2503.14182
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Bozhou and Song, Nan and Jin, Xin and Zhang, Li},
title = {Bridging Past and Future: End-to-End Autonomous Driving with Historical Prediction and Planning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {6854-6863}
}
|
End-to-end autonomous driving unifies tasks in a differentiable framework, enabling planning-oriented optimization and attracting growing attention.Current methods aggregate historical information either through dense historical bird's-eye-view (BEV) features or by querying a sparse memory bank, following paradigms inherited from detection.However, we argue that these paradigms either omit historical information in motion planning or fail to align with its multi-step nature, which requires predicting or planning multiple future time steps. In line with the philosophy of "future is a continuation of past", we propose **BridgeAD**, which reformulates motion and planning queries as multi-step queries to differentiate the queries for each future time step. This design enables the effective use of historical prediction and planning by applying them to the appropriate parts of the end-to-end system based on the time steps, which improves both perception and motion planning. Specifically, historical queries for the current frame are combined with perception, while queries for future frames are integrated with motion planning. In this way, we bridge the gap between past and future by aggregating historical insights at every time step, enhancing the overall coherence and accuracy of the end-to-end autonomous driving pipeline. Extensive experiments on the nuScenes dataset in both open-loop and closed-loop settings demonstrate that BridgeAD achieves state-of-the-art performance. We will make our code and models publicly available.
|
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417 |
Blood Flow Speed Estimation with Optical Coherence Tomography Angiography Images
|
[
"Wensheng Cheng",
"Zhenghong Li",
"Jiaxiang Ren",
"Hyomin Jeong",
"Congwu Du",
"Yingtian Pan",
"Haibin Ling"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_Blood_Flow_Speed_Estimation_with_Optical_Coherence_Tomography_Angiography_Images_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_Blood_Flow_Speed_Estimation_with_Optical_Coherence_Tomography_Angiography_Images_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_Blood_Flow_Speed_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Cheng_2025_CVPR,
author = {Cheng, Wensheng and Li, Zhenghong and Ren, Jiaxiang and Jeong, Hyomin and Du, Congwu and Pan, Yingtian and Ling, Haibin},
title = {Blood Flow Speed Estimation with Optical Coherence Tomography Angiography Images},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10466-10475}
}
|
Estimating blood flow speed is essential in many medical and physiological applications, yet it is extremely challenging due to complex vascular structure and flow dynamics, particularly for cerebral cortex regions. Existing techniques, such as Optical Doppler Tomography (ODT), generally require complex hardware control and signal processing, and still suffer from inherent system-level artifacts. To address these challenges, we propose a new learning-based approach named OCTA-Flow, which directly estimates vascular blood flow speed from Optical Coherence Tomography Angiography (OCTA) images that are commonly used for vascular structure analysis. OCTA-Flow employs several novel components to achieve this goal. First, using an encoder-decoder architecture, OCTA-Flow leverages ODT data as pseudo labels during training, thus bypassing the difficulty of collecting ground truth data. Second, to capture the relationship between vessels of varying scales and their flow speed, we design an Adaptive Window Fusion module that employs multiscale window attention. Third, to mitigate ODT artifacts, we incorporate a Conditional Random Field Decoder that promotes smoothness and consistency in the estimated blood flow. Together, these innovations enable OCTA-Flow to effectively produce accurate flow estimation, suppress the artifacts in ODT, and enhance practicality, benefiting from the established techniques of OCTA data acquisition. The code and data are available at https://github.com/Spritea/OCTA-Flow.
|
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] |
418 |
DreamTrack: Dreaming the Future for Multimodal Visual Object Tracking
|
[
"Mingzhe Guo",
"Weiping Tan",
"Wenyu Ran",
"Liping Jing",
"Zhipeng Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_DreamTrack_Dreaming_the_Future_for_Multimodal_Visual_Object_Tracking_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_DreamTrack_Dreaming_the_Future_for_Multimodal_Visual_Object_Tracking_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_DreamTrack_Dreaming_the_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Guo_2025_CVPR,
author = {Guo, Mingzhe and Tan, Weiping and Ran, Wenyu and Jing, Liping and Zhang, Zhipeng},
title = {DreamTrack: Dreaming the Future for Multimodal Visual Object Tracking},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7201-7210}
}
|
Aiming to achieve class-agnostic perception in visual object tracking, current trackers commonly formulate tracking as a one-shot detection problem with the template-matching architecture. Despite the success, severe environmental variations in long-term tracking raise challenges to generalizing the tracker in novel situations. Temporal trackers try to fix it by preserving the time-validity of target information with historical predictions, e.g., updating the template. However, solely transmitting the previous observations instead of learning from them leads to an inferior capability of understanding the tracking scenario from past experience, which is critical for the generalization in new frames. To address this issue, we reformulate temporal learning in visual tracking as a History-to-Future process and propose a novel tracking framework DreamTrack. Our DreamTrack learns the temporal dynamics from past observations to dream the future variations of the environment, which boosts the generalization with the extended future information from history. Considering the uncertainty of future variation, multimodal prediction is designed to infer the target trajectory of each possible future situation. The experiments demonstrate that our DreamTrack achieves leading performance with real-time inference speed. In particular, DreamTrack obtains SUC scores of 76.6%/87.9% on LaSOT/TrackingNet, surpassing all recent SOTA trackers.
|
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] |
419 |
OmniStyle: Filtering High Quality Style Transfer Data at Scale
|
[
"Ye Wang",
"Ruiqi Liu",
"Jiang Lin",
"Fei Liu",
"Zili Yi",
"Yilin Wang",
"Rui Ma"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_OmniStyle_Filtering_High_Quality_Style_Transfer_Data_at_Scale_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_OmniStyle_Filtering_High_Quality_Style_Transfer_Data_at_Scale_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_OmniStyle_Filtering_High_CVPR_2025_supplemental.pdf
|
2505.14028
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Ye and Liu, Ruiqi and Lin, Jiang and Liu, Fei and Yi, Zili and Wang, Yilin and Ma, Rui},
title = {OmniStyle: Filtering High Quality Style Transfer Data at Scale},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7847-7856}
}
|
In this paper, we introduce OmniStyle-1M, a large-scale paired style transfer dataset comprising over one million content-style-stylized image triplets across 1,000 diverse style categories, each enhanced with textual descriptions and instruction prompts. We show that OmniStyle-1M can not only improve the generalization of style transfer models through supervised training but also facilitates precise control over target stylization. Especially, to ensure the quality of the dataset, we introduce OmniFilter, a comprehensive style transfer quality assessment framework, which filters high-quality triplets based on content preservation, style consistency, and aesthetic appeal. Building upon this foundation, we propose OmniStyle, a framework based on the Diffusion Transformer (DiT) architecture designed for high-quality and efficient style transfer. This framework supports both instruction-guided and image-guided style transfer, generating high resolution outputs with exceptional detail. Extensive qualitative and quantitative evaluations demonstrate OmniStyle's superior performance compared to existing approaches, highlighting its efficiency and versatility. OmniStyle-1M and its accompanying methodologies provide a significant contribution to advancing high-quality style transfer, offering a valuable resource for the research community.
|
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] |
420 |
EIDT-V: Exploiting Intersections in Diffusion Trajectories for Model-Agnostic, Zero-Shot, Training-Free Text-to-Video Generation
|
[
"Diljeet Jagpal",
"Xi Chen",
"Vinay P. Namboodiri"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jagpal_EIDT-V_Exploiting_Intersections_in_Diffusion_Trajectories_for_Model-Agnostic_Zero-Shot_Training-Free_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jagpal_EIDT-V_Exploiting_Intersections_in_Diffusion_Trajectories_for_Model-Agnostic_Zero-Shot_Training-Free_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jagpal_EIDT-V_Exploiting_Intersections_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Jagpal_2025_CVPR,
author = {Jagpal, Diljeet and Chen, Xi and Namboodiri, Vinay P.},
title = {EIDT-V: Exploiting Intersections in Diffusion Trajectories for Model-Agnostic, Zero-Shot, Training-Free Text-to-Video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18219-18228}
}
|
Zero-shot, training-free, image-based text-to-video generation is an emerging area that aims to generate videos using existing image-based diffusion models. Current methods in this space require specific architectural changes to image-generation models, which limit their adaptability and scalability.In contrast to such methods, we provide a model-agnostic approach. We use intersections in diffusion trajectories, working only with the latent values. We could not obtain localized frame-wise coherence and diversity using only the intersection of trajectories. Thus, we instead use a grid-based approach. An in-context trained LLM is used to generate coherent frame-wise prompts; another is used to identify differences between frames. Based on these, we obtain a CLIP-based attention mask that controls the timing of switching the prompts for each grid cell. Earlier switching results in higher variance, while later switching results in more coherence. Therefore, Our approach can ensure appropriate control between coherence and variance for the frames.Our approach results in state-of-the-art performance while being more flexible when working with diverse image-generation models. The empirical analysis using quantitative metrics and user studies confirms our model's superior temporal consistency, visual fidelity and user satisfaction, thus providing a novel way to obtain training-free, image-based text-to-video generation.
|
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] |
421 |
Cross-View Completion Models are Zero-shot Correspondence Estimators
|
[
"Honggyu An",
"Jin Hyeon Kim",
"Seonghoon Park",
"Jaewoo Jung",
"Jisang Han",
"Sunghwan Hong",
"Seungryong Kim"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/An_Cross-View_Completion_Models_are_Zero-shot_Correspondence_Estimators_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/An_Cross-View_Completion_Models_are_Zero-shot_Correspondence_Estimators_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/An_Cross-View_Completion_Models_CVPR_2025_supplemental.pdf
|
2412.09072
|
@InProceedings{An_2025_CVPR,
author = {An, Honggyu and Kim, Jin Hyeon and Park, Seonghoon and Jung, Jaewoo and Han, Jisang and Hong, Sunghwan and Kim, Seungryong},
title = {Cross-View Completion Models are Zero-shot Correspondence Estimators},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1103-1115}
}
|
In this work, we analyze new aspects of cross-view completion, mainly through the analogy of cross-view completion and traditional self-supervised correspondence learning algorithms. Based on our analysis, we reveal that the cross-attention map of Croco-v2, best reflects this correspondence information compared to other correlations from the encoder or decoder features. We further verify the effectiveness of the cross-attention map by evaluating on both zero-shot and supervised dense geometric correspondence and multi-frame depth estimation.
|
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] |
422 |
Multi-party Collaborative Attention Control for Image Customization
|
[
"Han Yang",
"Chuanguang Yang",
"Qiuli Wang",
"Zhulin An",
"Weilun Feng",
"Libo Huang",
"Yongjun Xu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Multi-party_Collaborative_Attention_Control_for_Image_Customization_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Multi-party_Collaborative_Attention_Control_for_Image_Customization_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Multi-party_Collaborative_Attention_CVPR_2025_supplemental.pdf
|
2505.01428
|
@InProceedings{Yang_2025_CVPR,
author = {Yang, Han and Yang, Chuanguang and Wang, Qiuli and An, Zhulin and Feng, Weilun and Huang, Libo and Xu, Yongjun},
title = {Multi-party Collaborative Attention Control for Image Customization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7942-7951}
}
|
The rapid development of diffusion models has fueled a growing demand for customized image generation. However, current customization methods face several limitations: 1) typically accept either image or text conditions alone; 2) customization in complex visual scenarios often leads to subject leakage or confusion; 3) image-conditioned outputs tend to suffer from inconsistent backgrounds; and 4) high computational costs. To address these issues, this paper introduces Multi-party Collaborative Attention Control (MCA-Ctrl), a tuning-free approach that enables high-quality image customization under both text and complex visual conditions. Specifically, MCA-Ctrl leverages two key operations within the self-attention layer to coordinate multiple parallel diffusion processes and guide the target image generation. This approach allows MCA-Ctrl to capture the content and appearance of specific subjects while maintaining semantic consistency with the conditional input. Additionally, to mitigate subject leakage and confusion issues common in complex visual scenarios, we introduce a Subject Localization Module that extracts precise subject and editable image layers based on user instructions. Extensive quantitative and human evaluation experiments demonstrate that MCA-Ctrl outperforms previous methods in zero-shot image customization, effectively addressing the aforementioned issues.
|
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] |
423 |
Reproducible Vision-Language Models Meet Concepts Out of Pre-Training
|
[
"Ziliang Chen",
"Xin Huang",
"Xiaoxuan Fan",
"Keze Wang",
"Yuyu Zhou",
"Quanlong Guan",
"Liang Lin"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Reproducible_Vision-Language_Models_Meet_Concepts_Out_of_Pre-Training_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Reproducible_Vision-Language_Models_Meet_Concepts_Out_of_Pre-Training_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Reproducible_Vision-Language_Models_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Chen_2025_CVPR,
author = {Chen, Ziliang and Huang, Xin and Fan, Xiaoxuan and Wang, Keze and Zhou, Yuyu and Guan, Quanlong and Lin, Liang},
title = {Reproducible Vision-Language Models Meet Concepts Out of Pre-Training},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14701-14711}
}
|
Contrastive Language-Image Pre-training (CLIP) models as a milestone of modern multimodal intelligence, its generalization mechanism grasped massive research interests in the community. While existing studies limited in the scope of pre-training knowledge, hardly underpinned its generalization to countless open-world concepts absent from the pre-training regime. This paper dives into such Out-of-Pre-training (OOP) generalization problem from a holistic perspective. We propose LAION-Beyond benchmark to isolate the evaluation of OOP concepts from pre-training knowledge, with regards to OpenCLIP and its reproducible variants derived from LAION datasets. Empirical analysis evidences that despite image features of OOP concepts born with significant category margins, their zero-shot transfer significantly fails due to the poor image-text alignment. To this, we elaborate the "name-tuning" methodology with its theoretical merits in terms of OOP generalization, then propose few-shot name learning (FSNL) and zero-shot name learning (ZSNL) algorithms to achieve OOP generalization in a data-efficient manner. Their superiority have been further verified in our comprehensive experiments.
|
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] |
424 |
Through-The-Mask: Mask-based Motion Trajectories for Image-to-Video Generation
|
[
"Guy Yariv",
"Yuval Kirstain",
"Amit Zohar",
"Shelly Sheynin",
"Yaniv Taigman",
"Yossi Adi",
"Sagie Benaim",
"Adam Polyak"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yariv_Through-The-Mask_Mask-based_Motion_Trajectories_for_Image-to-Video_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yariv_Through-The-Mask_Mask-based_Motion_Trajectories_for_Image-to-Video_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yariv_Through-The-Mask_Mask-based_Motion_CVPR_2025_supplemental.zip
| null |
@InProceedings{Yariv_2025_CVPR,
author = {Yariv, Guy and Kirstain, Yuval and Zohar, Amit and Sheynin, Shelly and Taigman, Yaniv and Adi, Yossi and Benaim, Sagie and Polyak, Adam},
title = {Through-The-Mask: Mask-based Motion Trajectories for Image-to-Video Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18198-18208}
}
|
We consider the task of Image-to-Video (I2V) generation, which involves transforming static images into realistic video sequences based on a textual description. While recent advancements produce photorealistic outputs, they frequently struggle to create videos with accurate and consistent object motion, especially in multi-object scenarios. To address these limitations, we propose a two-stage compositional framework that decomposes I2V generation into: (i) An explicit intermediate representation generation stage, followed by (ii) A video generation stage that is conditioned on this representation. Our key innovation is the introduction of a mask-based motion trajectory as an intermediate representation, that captures both semantic object information and motion, enabling an expressive but compact representation of motion and semantics. To incorporate the learned representation in the second stage, we utilize object-level attention objectives. Specifically, we consider a spatial, per-object, masked-cross attention objective, integrating object-specific prompts into corresponding latent space regions and a masked spatio-temporal self-attention objective, ensuring frame-to-frame consistency for each object. We evaluate our method on challenging benchmarks with multi-object and high-motion scenarios and empirically demonstrate that the proposed method achieves state-of-the-art results in temporal coherence, motion realism, and text-prompt faithfulness. Additionally, we introduce SA-V-128, a new challenging benchmark for single-object and multi-object I2V generation, and demonstrate our method's superiority on this benchmark. Project page is available at https://guyyariv.github.io/TTM/
|
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] |
425 |
MAGE : Single Image to Material-Aware 3D via the Multi-View G-Buffer Estimation Model
|
[
"Haoyuan Wang",
"Zhenwei Wang",
"Xiaoxiao Long",
"Cheng Lin",
"Gerhard Hancke",
"Rynson W.H. Lau"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MAGE__Single_Image_to_Material-Aware_3D_via_the_Multi-View_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MAGE__Single_Image_to_Material-Aware_3D_via_the_Multi-View_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MAGE__Single_CVPR_2025_supplemental.zip
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Haoyuan and Wang, Zhenwei and Long, Xiaoxiao and Lin, Cheng and Hancke, Gerhard and Lau, Rynson W.H.},
title = {MAGE : Single Image to Material-Aware 3D via the Multi-View G-Buffer Estimation Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10985-10995}
}
|
With advances in deep learning models and the availability of large-scale 3D datasets, we have recently witnessed significant progress in single-view 3D reconstruction. However, existing methods often fail to reconstruct physically based material properties given a single image, limiting their applicability in complicated scenarios. This paper presents a novel approach (named MAGE) for generating 3D geometry with realistic decomposed material properties given a single image as input. Our method leverages inspiration from traditional computer graphics deferred rendering pipelines to introduce a multi-view G-buffer estimation model. The proposed model estimates G-buffers for various views as multi-domain images, including XYZ coordinates, normals, albedo, roughness, and metallic properties from a single-view RGB image. To address the inherent ambiguity and inconsistency in generating G-buffers simultaneously, we also formulate a deterministic network from the pretrained diffusion models and propose a lighting response loss that enforces consistency across these domains using PBR principles. Finally, we propose a large-scale synthetic dataset rich in material diversity for our model training. Experimental results demonstrate the effectiveness of our method in producing high-quality 3D meshes with rich material properties. Our code and dataset can be found at https://www.whyy.site/paper/mage.
|
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] |
426 |
Segment Anything, Even Occluded
|
[
"Wei-En Tai",
"Yu-Lin Shih",
"Cheng Sun",
"Yu-Chiang Frank Wang",
"Hwann-Tzong Chen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Tai_Segment_Anything_Even_Occluded_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Tai_Segment_Anything_Even_Occluded_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tai_Segment_Anything_Even_CVPR_2025_supplemental.pdf
|
2503.06261
|
@InProceedings{Tai_2025_CVPR,
author = {Tai, Wei-En and Shih, Yu-Lin and Sun, Cheng and Wang, Yu-Chiang Frank and Chen, Hwann-Tzong},
title = {Segment Anything, Even Occluded},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29385-29394}
}
|
Amodal instance segmentation, which aims to detect and segment both visible and invisible parts of objects in images, plays a crucial role in various applications, including autonomous driving, robotic manipulation, and scene understanding. While existing methods require training both front-end detectors and mask decoders jointly, this approach lacks flexibility and fails to leverage the strengths of pre-existing modal detectors. To address this limitation, we propose SAMEO, a novel framework that adapts the Segment Anything Model (SAM) as a versatile mask decoder capable of interfacing with various front-end detectors to enable mask prediction even for partially occluded objects. Acknowledging the constraints of limited amodal segmentation datasets, we introduce Amodal-LVIS, a large-scale synthetic dataset comprising 300K images derived from the modal LVIS and LVVIS datasets. This dataset significantly expands the training data available for amodal segmentation research. Our experimental results demonstrate that our approach, when trained on the newly extended dataset, including Amodal-LVIS, achieves remarkable zero-shot performance on both COCOA-cls and D2SA benchmarks, highlighting its potential for generalization to unseen scenarios.
|
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] |
427 |
HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos
|
[
"Prithviraj Banerjee",
"Sindi Shkodrani",
"Pierre Moulon",
"Shreyas Hampali",
"Shangchen Han",
"Fan Zhang",
"Linguang Zhang",
"Jade Fountain",
"Edward Miller",
"Selen Basol",
"Richard Newcombe",
"Robert Wang",
"Jakob Julian Engel",
"Tomas Hodan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Banerjee_HOT3D_Hand_and_Object_Tracking_in_3D_from_Egocentric_Multi-View_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Banerjee_HOT3D_Hand_and_Object_Tracking_in_3D_from_Egocentric_Multi-View_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Banerjee_HOT3D_Hand_and_CVPR_2025_supplemental.pdf
|
2411.19167
|
@InProceedings{Banerjee_2025_CVPR,
author = {Banerjee, Prithviraj and Shkodrani, Sindi and Moulon, Pierre and Hampali, Shreyas and Han, Shangchen and Zhang, Fan and Zhang, Linguang and Fountain, Jade and Miller, Edward and Basol, Selen and Newcombe, Richard and Wang, Robert and Engel, Jakob Julian and Hodan, Tomas},
title = {HOT3D: Hand and Object Tracking in 3D from Egocentric Multi-View Videos},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7061-7071}
}
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We introduce HOT3D, a publicly available dataset for egocentric hand and object tracking in 3D. The dataset offers over 833 minutes (3.7M+ images) of recordings that feature 19 subjects interacting with 33 diverse rigid objects. In addition to simple pick-up, observe, and put-down actions, the subjects perform actions typical for a kitchen, office, and living room environment. The recordings include multiple synchronized data streams containing egocentric multi-view RGB/monochrome images, eye gaze signal, scene point clouds, and 3D poses of cameras, hands, and objects. The dataset is recorded with two headsets from Meta: Project Aria, which is a research prototype of AI glasses, and Quest 3, a virtual-reality headset that has shipped millions of units. Ground-truth poses were obtained by a motion-capture system using small optical markers attached to hands and objects. Hand annotations are provided in the UmeTrack and MANO formats, and objects are represented by 3D meshes with PBR materials obtained by an in-house scanner. In our experiments, we demonstrate the effectiveness of multi-view egocentric data for three popular tasks: 3D hand tracking, model-based 6DoF object pose estimation, and 3D lifting of unknown in-hand objects. The evaluated multi-view methods, whose benchmarking is uniquely enabled by HOT3D, significantly outperform their single-view counterparts.
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] |
428 |
DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation
|
[
"Zhiqiang Shen",
"Ammar Sherif",
"Zeyuan Yin",
"Shitong Shao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_DELT_A_Simple_Diversity-driven_EarlyLate_Training_for_Dataset_Distillation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_DELT_A_Simple_Diversity-driven_EarlyLate_Training_for_Dataset_Distillation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_DELT_A_Simple_CVPR_2025_supplemental.pdf
|
2411.19946
|
@InProceedings{Shen_2025_CVPR,
author = {Shen, Zhiqiang and Sherif, Ammar and Yin, Zeyuan and Shao, Shitong},
title = {DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4797-4806}
}
|
Recent advances in dataset distillation have led to solutions in two main directions. The conventional batch-to-batch matching mechanism is ideal for small-scale datasets and includes bi-level optimization methods on models and syntheses, such as FRePo, RCIG, and RaT-BPTT, as well as other methods like distribution matching, gradient matching, and weight trajectory matching. Conversely, batch-to-global matching typifies decoupled methods, which are particularly advantageous for large-scale datasets. This approach has garnered substantial interest within the community, as seen in SRe^2L, G-VBSM, WMDD, and CDA. A primary challenge with the second approach is the lack of diversity among syntheses within each class since samples are optimized independently and the same global supervision signals are reused across different synthetic images. In this study, we propose a new Diversity-driven EarlyLate Training (DELT) scheme to enhance the diversity of images in batch-to-global matching with less computation. Our approach is conceptually simple yet effective, it partitions predefined IPC samples into smaller subtasks and employs local optimizations to distill each subset into distributions from distinct phases, reducing the uniformity induced by the unified optimization process. These distilled images from the subtasks demonstrate effective generalization when applied to the entire task. We conduct extensive experiments on CIFAR, Tiny-ImageNet, ImageNet-1K, and its sub-datasets. Our approach outperforms the previous state-of-the-art by 2~5% on average across different datasets and IPCs (images per class), increasing diversity per class by more than 5% while reducing synthesis time by up to 39.3% for enhancing the training efficiency.
|
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] |
429 |
MESC-3D:Mining Effective Semantic Cues for 3D Reconstruction from a Single Image
|
[
"Shaoming Li",
"Qing Cai",
"Songqi Kong",
"Runqing Tan",
"Heng Tong",
"Shiji Qiu",
"Yongguo Jiang",
"Zhi Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MESC-3DMining_Effective_Semantic_Cues_for_3D_Reconstruction_from_a_Single_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_MESC-3DMining_Effective_Semantic_Cues_for_3D_Reconstruction_from_a_Single_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_MESC-3DMining_Effective_Semantic_CVPR_2025_supplemental.zip
| null |
@InProceedings{Li_2025_CVPR,
author = {Li, Shaoming and Cai, Qing and Kong, Songqi and Tan, Runqing and Tong, Heng and Qiu, Shiji and Jiang, Yongguo and Liu, Zhi},
title = {MESC-3D:Mining Effective Semantic Cues for 3D Reconstruction from a Single Image},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16912-16921}
}
|
Reconstructing 3D shapes from a single image plays an important role in computer vision. Many methods have been proposed and achieve impressive performance. However, existing methods mainly focus on extracting semantic information from images and then simply concatenating it with 3D point clouds without further exploring the concatenated semantics. As a result, these entangled semantic features significantly hinder the reconstruction performance. In this paper, we propose a novel single-image 3D reconstruction method called Mining Effective Semantic Cues for 3D Reconstruction from a Single Image (MESC-3D), which can actively mine effective semantic cues from entangled features. Specifically, we design an Effective Semantic Mining Module, which incorporates a point-selection map establish connections between point clouds and semantic attributes, enabling the point clouds to autonomously select the necessary information. Furthermore, to address the potential insufficiencies in semantic information from a single image, such as occlusions, inspired by the human ability to represent 3D objects using prior knowledge drawn from daily experiences, we introduce a 3D Semantic Prior Learning Module. This module employs contrastive learning paradigm along with a learnable text prompt to incorporates semantic understanding of spatial structures, enabling the model to interpret and reconstruct 3D objects with greater accuracy and realism, closely mirroring human perception of complex 3D environments. Extensive evaluations show that our method achieves significant improvements in reconstruction quality and robustness compared to prior works. Additionally, further experiments validate the strong generalization capabilities and excels in zero-shot preformance on unseen classes. Code is available at https://github.com/QINGQINGLE/MESC-3D.
|
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] |
430 |
RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete
|
[
"Yuheng Ji",
"Huajie Tan",
"Jiayu Shi",
"Xiaoshuai Hao",
"Yuan Zhang",
"Hengyuan Zhang",
"Pengwei Wang",
"Mengdi Zhao",
"Yao Mu",
"Pengju An",
"Xinda Xue",
"Qinghang Su",
"Huaihai Lyu",
"Xiaolong Zheng",
"Jiaming Liu",
"Zhongyuan Wang",
"Shanghang Zhang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ji_RoboBrain_A_Unified_Brain_Model_for_Robotic_Manipulation_from_Abstract_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ji_RoboBrain_A_Unified_Brain_Model_for_Robotic_Manipulation_from_Abstract_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ji_RoboBrain_A_Unified_CVPR_2025_supplemental.pdf
|
2502.21257
|
@InProceedings{Ji_2025_CVPR,
author = {Ji, Yuheng and Tan, Huajie and Shi, Jiayu and Hao, Xiaoshuai and Zhang, Yuan and Zhang, Hengyuan and Wang, Pengwei and Zhao, Mengdi and Mu, Yao and An, Pengju and Xue, Xinda and Su, Qinghang and Lyu, Huaihai and Zheng, Xiaolong and Liu, Jiaming and Wang, Zhongyuan and Zhang, Shanghang},
title = {RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1724-1734}
}
|
Recent advancements in Multimodal Large Language Models (MLLMs) have shown remarkable capabilities across various multimodal contexts. However, their application in robotic scenarios, particularly for long-horizon manipulation tasks, reveals significant limitations. These limitations arise from the current MLLMs lacking three essential robotic brain capabilities: Planning Capability, which involves decomposing complex manipulation instructions into manageable sub-tasks; Affordance Perception, the ability to recognize and interpret the affordances of interactive objects; and Trajectory Prediction, the foresight to anticipate the complete manipulation trajectory necessary for successful execution. To enhance the robotic brain's core capabilities from abstract to concrete, we introduce ShareRobot, a high-quality heterogeneous dataset that labels multi-dimensional information such as task planning, object affordance, and end-effector trajectory. ShareRobot's diversity and accuracy have been meticulously refined by three human annotators. Building on this dataset, we developed RoboBrain, an MLLM-based model that combines robotic and general multi-modal data, utilizes a multi-stage training strategy, and incorporates long videos and high-resolution images to improve its robotic manipulation capabilities.Extensive experiments demonstrate that RoboBrain achieves state-of-the-art performance across various obotic tasks, highlighting its potential to advance robotic brain capabilities.
|
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] |
431 |
Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging
|
[
"Xianrui Li",
"Yufei Cui",
"Jun Li",
"Antoni B. Chan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Advancing_Multiple_Instance_Learning_with_Continual_Learning_for_Whole_Slide_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Advancing_Multiple_Instance_Learning_with_Continual_Learning_for_Whole_Slide_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Advancing_Multiple_Instance_CVPR_2025_supplemental.zip
|
2505.10649
|
@InProceedings{Li_2025_CVPR,
author = {Li, Xianrui and Cui, Yufei and Li, Jun and Chan, Antoni B.},
title = {Advancing Multiple Instance Learning with Continual Learning for Whole Slide Imaging},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20800-20809}
}
|
Advances in medical imaging and deep learning have propelled progress in whole slide image (WSI) analysis, with multiple instance learning (MIL) showing promise for efficient and accurate diagnostics. However, conventional MIL models often lack adaptability to evolving datasets, as they rely on static training that cannot incorporate new information without extensive retraining. Applying continual learning (CL) to MIL models is a possible solution, but often sees limited improvements. In this paper, we analyze CL in the context of attention MIL models and find that the model forgetting is mainly concentrated in the attention layers of the MIL model. Using the results of this analysis we propose two components for improving CL on MIL:Attention Knowledge Distillation (AKD) and the Pseudo-Bag Memory Pool (PMP). AKD mitigates catastrophic forgetting by focusing on retaining attention layer knowledge between learning sessions, while PMP reduces the memory footprint by selectively storing only the most informative patches, or "pseudo-bags" from WSIs. Experimental evaluations demonstrate that our method significantly improves both accuracy and memory efficiency on diverse WSI datasets, outperforming current state-of-the-art CL methods. This work provides a foundation for CL in large-scale, weakly annotated clinical datasets, paving the way for more adaptable and resilient diagnostic models.
|
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] |
432 |
Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning
|
[
"Dongyao Jiang",
"Haodong Jing",
"Yongqiang Ma",
"Nanning Zheng"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Beyond_Image_Classification_A_Video_Benchmark_and_Dual-Branch_Hybrid_Discrimination_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Beyond_Image_Classification_A_Video_Benchmark_and_Dual-Branch_Hybrid_Discrimination_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Beyond_Image_Classification_CVPR_2025_supplemental.zip
| null |
@InProceedings{Jiang_2025_CVPR,
author = {Jiang, Dongyao and Jing, Haodong and Ma, Yongqiang and Zheng, Nanning},
title = {Beyond Image Classification: A Video Benchmark and Dual-Branch Hybrid Discrimination Framework for Compositional Zero-Shot Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9860-9869}
}
|
Human reasoning naturally combines concepts to identify unseen compositions, a capability that Compositional Zero-Shot Learning (CZSL) aims to replicate in machine learning models. However, we observe that focusing solely on typical image classification tasks in CZSL may limit models' compositional generalization potential. To address this, we introduce C-EgoExo, a video-based benchmark, along with a compositional action recognition task to enable more comprehensive evaluations. Inspired by human reasoning processes, we propose a Dual-branch Hybrid Discrimination (DHD) framework, featuring two branches that decode visual inputs in distinct observation sequences. Through a cross-attention mechanism and a contextual dependency encoder, DHD effectively mitigates challenges posed by conditional variance. We further design a Copula-based orthogonal decoding loss to counteract contextual interference in primitive decoding. Our approach demonstrates outstanding performance across diverse CZSL tasks, excelling in both image-based and video-based modalities and in attribute-object and action-object compositions, setting a new benchmark for CZSL evaluation.
|
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] |
433 |
ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects
|
[
"Woojin Lee",
"Hyugjae Chang",
"Jaeho Moon",
"Jaehyup Lee",
"Munchurl Kim"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_ABBSPO_Adaptive_Bounding_Box_Scaling_and_Symmetric_Prior_based_Orientation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_ABBSPO_Adaptive_Bounding_Box_Scaling_and_Symmetric_Prior_based_Orientation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_ABBSPO_Adaptive_Bounding_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Lee_2025_CVPR,
author = {Lee, Woojin and Chang, Hyugjae and Moon, Jaeho and Lee, Jaehyup and Kim, Munchurl},
title = {ABBSPO: Adaptive Bounding Box Scaling and Symmetric Prior based Orientation Prediction for Detecting Aerial Image Objects},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8848-8858}
}
|
Weakly supervised Oriented Object Detection (WS-OOD) has gained attention as a cost-effective alternative to fully supervised methods, providing efficiency and high accuracy. Among weakly supervised approaches, horizontal bounding box (HBox) supervised OOD stands out for its ability to directly leverage existing HBox annotations while achieving the highest accuracy under weak supervision settings. This paper introduces adaptive bounding box scaling and symmetry-prior-based orientation prediction, called ABBSPO that is a framework for WS-OOD. Our ABBSPO addresses the limitations of previous HBox-supervised OOD methods, which compare ground truth (GT) HBoxes directly with predicted RBoxes' minimum circumscribed rectangles, often leading to inaccuracies. To overcome this, we propose: (i) Adaptive Bounding Box Scaling (ABBS) that appropriately scales the GT HBoxes to optimize for the size of each predicted RBox, ensuring more accurate prediction for RBoxes' scales; and (ii) a Symmetric Prior Angle (SPA) loss that uses the inherent symmetry of aerial objects for self-supervised learning, addressing the issue in previous methods where learning fails if they consistently make incorrect predictions for all three augmented views (original, rotated, and flipped). Extensive experimental results demonstrate that our ABBSPO achieves state-of-the-art results, outperforming existing methods.
|
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] |
434 |
Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks
|
[
"Yu Zhou",
"Dian Zheng",
"Qijie Mo",
"Renjie Lu",
"Kun-Yu Lin",
"Wei-Shi Zheng"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Decoupled_Distillation_to_Erase_A_General_Unlearning_Method_for_Any_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Decoupled_Distillation_to_Erase_A_General_Unlearning_Method_for_Any_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Decoupled_Distillation_to_CVPR_2025_supplemental.pdf
|
2503.23751
|
@InProceedings{Zhou_2025_CVPR,
author = {Zhou, Yu and Zheng, Dian and Mo, Qijie and Lu, Renjie and Lin, Kun-Yu and Zheng, Wei-Shi},
title = {Decoupled Distillation to Erase: A General Unlearning Method for Any Class-centric Tasks},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {20350-20359}
}
|
In this work, we present DEcoupLEd Distillation To Erase (DELETE), a general and strong unlearning method for any class-centric tasks. To derive this, we first propose a theoretical framework to analyze the general form of unlearning loss and decompose it into forgetting and retention terms. Through the theoretical framework, we point out that a class of previous methods could be mainly formulated as a loss that implicitly optimizes the forgetting term while lacking supervision for the retention term, disturbing the distribution of pre-trained model and struggling to adequately preserve knowledge of the remaining classes. To address it, we refine the retention term using "dark knowledge" and propose a mask distillation unlearning method. By applying a mask to separate forgetting logits from retention logits, our approach optimizes both the forgetting and refined retention components simultaneously, retaining knowledge of the remaining classes while ensuring thorough forgetting of the target class. Without access to the remaining data or intervention (i.e., used in some works), we achieve state-of-the-art performance across various benchmarks. What's more, DELETE is a general solution that can be applied to various downstream tasks, including face recognition, backdoor defense, and semantic segmentation with great performance.
|
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] |
435 |
TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions
|
[
"Wang Yu-Hang",
"Junkang Guo",
"Aolei Liu",
"Kaihao Wang",
"Zaitong Wu",
"Zhenyu Liu",
"Wenfei Yin",
"Jian Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yu-Hang_TAET_Two-Stage_Adversarial_Equalization_Training_on_Long-Tailed__Distributions_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu-Hang_TAET_Two-Stage_Adversarial_Equalization_Training_on_Long-Tailed__Distributions_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu-Hang_TAET_Two-Stage_Adversarial_CVPR_2025_supplemental.zip
| null |
@InProceedings{Yu-Hang_2025_CVPR,
author = {Yu-Hang, Wang and Guo, Junkang and Liu, Aolei and Wang, Kaihao and Wu, Zaitong and Liu, Zhenyu and Yin, Wenfei and Liu, Jian},
title = {TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15476-15485}
}
|
Adversarial robustness remains a significant challenge in deploying deep neural networks for real-world applications. While adversarial training is widely acknowledged as a promising defense strategy, most existing studies primarily focus on balanced datasets, neglecting the fact that real-world data often exhibit a long-tailed distribution, which introduces substantial challenges to robustness. In this paper, we provide an in-depth analysis of adversarial training in the context of long-tailed distributions and identify the limitations of the current state-of-the-art method, AT-BSL, in achieving robust performance under such conditions. To address these challenges, we propose a novel training framework, TAET, which incorporates an initial stabilization phase followed by a stratified, equalization adversarial training phase. Furthermore, prior work on long-tailed robustness has largely overlooked a crucial evaluation metric--Balanced Accuracy. To fill this gap, we introduce the concept of Balanced Robustness, a comprehensive metric that measures robustness specifically under long-tailed distributions. Extensive experiments demonstrate that our method outperforms existing advanced defenses, yielding significant improvements in both memory and computational efficiency. We believe this work represents a substantial step forward in tackling robustness challenges in real-world applications. Our paper code can be found at https://github.com/BuhuiOK/TAET-Two-Stage-Adversarial-Equalization-Training-on-Long-Tailed-Distributions
|
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] |
436 |
Few-shot Personalized Scanpath Prediction
|
[
"Ruoyu Xue",
"Jingyi Xu",
"Sounak Mondal",
"Hieu Le",
"Greg Zelinsky",
"Minh Hoai",
"Dimitris Samaras"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_Few-shot_Personalized_Scanpath_Prediction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_Few-shot_Personalized_Scanpath_Prediction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_Few-shot_Personalized_Scanpath_CVPR_2025_supplemental.pdf
|
2504.05499
|
@InProceedings{Xue_2025_CVPR,
author = {Xue, Ruoyu and Xu, Jingyi and Mondal, Sounak and Le, Hieu and Zelinsky, Greg and Hoai, Minh and Samaras, Dimitris},
title = {Few-shot Personalized Scanpath Prediction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13497-13507}
}
|
A personalized model for scanpath prediction provides insights into the visual preferences and attention patterns of individual subjects. However, existing methods for training scanpath prediction models are data-intensive and cannot be effectively personalized to new individuals with only a few available examples. In this paper, we propose few-shot personalized scanpath prediction task (FS-PSP) and a novel method to address it, which aims to predict scanpaths for an unseen subject using minimal support data of that subject's scanpath behavior. The key to our method's adaptability is the Subject-Embedding Network (SE-Net), specifically designed to capture unique, individualized representations for each subject's scanpaths. SE-Net generates subject embeddings that effectively distinguish between subjects while minimizing variability among scanpaths from the same individual. The personalized scanpath prediction model is then conditioned on these subject embeddings to produce accurate, personalized results. Experiments on multiple eye-tracking datasets demonstrate that our method excels in FS-PSP settings and does not require any fine-tuning steps at test time. Code is available at: https://github.com/cvlab-stonybrook/few-shot-scanpath
|
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] |
437 |
Do Your Best and Get Enough Rest for Continual Learning
|
[
"Hankyul Kang",
"Gregor Seifer",
"Donghyun Lee",
"Jongbin Ryu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Kang_Do_Your_Best_and_Get_Enough_Rest_for_Continual_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Kang_Do_Your_Best_and_Get_Enough_Rest_for_Continual_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kang_Do_Your_Best_CVPR_2025_supplemental.pdf
|
2503.18371
|
@InProceedings{Kang_2025_CVPR,
author = {Kang, Hankyul and Seifer, Gregor and Lee, Donghyun and Ryu, Jongbin},
title = {Do Your Best and Get Enough Rest for Continual Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {10077-10086}
}
|
According to the forgetting curve theory, we can enhance memory retention by learning extensive data and taking adequate rest. This means that in order to effectively retain new knowledge, it is essential to learn it thoroughly and ensure sufficient rest so that our brain can memorize without forgetting. The main takeaway from this theory is that learning extensive data at once necessitates sufficient rest before learning the same data again. This aspect of human long-term memory retention can be effectively utilized to address the continual learning of neural networks. Retaining new knowledge for a long period of time without catastrophic forgetting is the critical problem of continual learning. Therefore, based on Ebbinghaus' theory, we introduce the view-batch model that adjusts the learning schedules to optimize the recall interval between retraining the same samples. The proposed view-batch model allows the network to get enough rest to learn extensive knowledge from the same samples with a recall interval of sufficient length. To this end, we specifically present two approaches: 1) a replay method that guarantees the optimal recall interval, and 2) a self-supervised learning that acquires extensive knowledge from a single training sample at a time. We empirically show that these approaches of our method are aligned with the forgetting curve theory, which can enhance long-term memory. In our experiments, we also demonstrate that our method significantly improves many state-of-the-art continual learning methods in various protocols and scenarios. We open-source this project at https://github.com/hankyul2/ViewBatchModel.
|
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] |
438 |
Enhancing Few-Shot Class-Incremental Learning via Training-Free Bi-Level Modality Calibration
|
[
"Yiyang Chen",
"Tianyu Ding",
"Lei Wang",
"Jing Huo",
"Yang Gao",
"Wenbin Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Enhancing_Few-Shot_Class-Incremental_Learning_via_Training-Free_Bi-Level_Modality_Calibration_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Enhancing_Few-Shot_Class-Incremental_Learning_via_Training-Free_Bi-Level_Modality_Calibration_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Enhancing_Few-Shot_Class-Incremental_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Chen_2025_CVPR,
author = {Chen, Yiyang and Ding, Tianyu and Wang, Lei and Huo, Jing and Gao, Yang and Li, Wenbin},
title = {Enhancing Few-Shot Class-Incremental Learning via Training-Free Bi-Level Modality Calibration},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {9881-9890}
}
|
Few-shot Class-Incremental Learning (FSCIL) challenges models to adapt to new classes with limited samples, presenting greater difficulties than traditional classincremental learning. While existing approaches rely heavily on visual models and require additional training during base or incremental phases, we propose a training-free framework that leverages pre-trained visual-language models like CLIP. At the core of our approach is a novel Bilevel Modality Calibration (BiMC) strategy. Our framework initially performs intra-modal calibration, combining LLM-generated fine-grained category descriptions with visual prototypes from the base session to achieve precise classifier estimation. This is further complemented by inter-modal calibration that fuses pre-trained linguistic knowledge with task-specific visual priors to mitigate modality-specific biases. To enhance prediction robustness, we introduce additional metrics and strategies that maximize the utilization of limited data. Extensive experimental results demonstrate that our approach significantly outperforms existing methods. Code is available at: https://github.com/yychen016/BiMC.
|
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] |
439 |
MUSt3R: Multi-view Network for Stereo 3D Reconstruction
|
[
"Yohann Cabon",
"Lucas Stoffl",
"Leonid Antsfeld",
"Gabriela Csurka",
"Boris Chidlovskii",
"Jerome Revaud",
"Vincent Leroy"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cabon_MUSt3R_Multi-view_Network_for_Stereo_3D_Reconstruction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cabon_MUSt3R_Multi-view_Network_for_Stereo_3D_Reconstruction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cabon_MUSt3R_Multi-view_Network_CVPR_2025_supplemental.zip
|
2503.01661
|
@InProceedings{Cabon_2025_CVPR,
author = {Cabon, Yohann and Stoffl, Lucas and Antsfeld, Leonid and Csurka, Gabriela and Chidlovskii, Boris and Revaud, Jerome and Leroy, Vincent},
title = {MUSt3R: Multi-view Network for Stereo 3D Reconstruction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1050-1060}
}
|
DUSt3R introduced a novel paradigm in geometric computer vision by proposing a model that can provide dense and unconstrained Stereo 3D Reconstruction of arbitrary image collections with no prior information about camera calibration nor viewpoint poses. Under the hood, however, DUSt3R processes image pairs, regressing local 3D reconstructions that need to be aligned in a global coordinate system. The number of pairs, growing quadratically, is an inherent limitation that becomes especially concerning for robust and fast optimization in the case of large image collections. In this paper, we propose an extension of DUSt3R from pairs to multiple views, that addresses all aforementioned concerns. Indeed, we propose a Multi-view Network for Stereo 3D Reconstruction, or MUSt3R, that modifies the DUSt3R architecture by making it symmetric and extending it to directly predict 3D structure for all views in a common coordinate frame. Second, we entail the model with a multi-layer memory mechanism which allows to reduce the computational complexity and to scale the reconstruction to large collections, inferring thousands of 3D pointmaps at high frame-rates with limited added complexity. The framework is designed to perform 3D reconstruction both offline and online, and hence can be seamlessly applied to SfM and visual SLAM scenarios showing state-of-the-art performance on various 3D downstream tasks, including uncalibrated Visual Odometry, relative camera pose, scale and focal estimation, 3D reconstruction and multi-view depth estimation.
|
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] |
440 |
Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models
|
[
"Zhihang Liu",
"Chen-Wei Xie",
"Pandeng Li",
"Liming Zhao",
"Longxiang Tang",
"Yun Zheng",
"Chuanbin Liu",
"Hongtao Xie"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hybrid-Level_Instruction_Injection_for_Video_Token_Compression_in_Multi-modal_Large_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hybrid-Level_Instruction_Injection_for_Video_Token_Compression_in_Multi-modal_Large_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Hybrid-Level_Instruction_Injection_CVPR_2025_supplemental.pdf
|
2503.16036
|
@InProceedings{Liu_2025_CVPR,
author = {Liu, Zhihang and Xie, Chen-Wei and Li, Pandeng and Zhao, Liming and Tang, Longxiang and Zheng, Yun and Liu, Chuanbin and Xie, Hongtao},
title = {Hybrid-Level Instruction Injection for Video Token Compression in Multi-modal Large Language Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8568-8578}
}
|
Recent Multi-modal Large Language Models (MLLMs) have been challenged by the computational overhead resulting from massive video frames, often alleviated through compression strategies. However, the visual content is not equally contributed to user instructions, existing strategies (e.g., average pool) inevitably lead to the loss of potentially useful information. To tackle this, we propose the Hybrid-level Instruction Injection Strategy for Conditional Token Compression in MLLMs (HICom), utilizing the instruction as a condition to guide the compression from both local and global levels. This encourages the compression to retain the maximum amount of user-focused information while reducing visual tokens to minimize computational burden. Specifically, the instruction condition is injected into the grouped visual tokens at the local level and the learnable tokens at the global level, and we conduct the attention mechanism to complete the conditional compression. From the hybrid-level compression, the instruction-relevant visual parts are highlighted while the temporal-spatial structure is also preserved for easier understanding of LLMs. To further unleash the potential of HICom, we introduce a new conditional pre-training stage with our proposed dataset HICom-248K. Experiments show that our HICom can obtain distinguished video understanding ability with fewer tokens, increasing the performance by 2.43% average on three multiple-choice QA benchmarks and saving 78.8% tokens compared with the SOTA method. The code is available at https://github.com/lntzm/HICom.
|
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] |
441 |
Mamba4D: Efficient 4D Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models
|
[
"Jiuming Liu",
"Jinru Han",
"Lihao Liu",
"Angelica I. Aviles-Rivero",
"Chaokang Jiang",
"Zhe Liu",
"Hesheng Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Mamba4D_Efficient_4D_Point_Cloud_Video_Understanding_with_Disentangled_Spatial-Temporal_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Mamba4D_Efficient_4D_Point_Cloud_Video_Understanding_with_Disentangled_Spatial-Temporal_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Mamba4D_Efficient_4D_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Liu_2025_CVPR,
author = {Liu, Jiuming and Han, Jinru and Liu, Lihao and Aviles-Rivero, Angelica I. and Jiang, Chaokang and Liu, Zhe and Wang, Hesheng},
title = {Mamba4D: Efficient 4D Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17626-17636}
}
|
Point cloud videos can faithfully capture real-world spatial geometries and temporal dynamics, which are essential for enabling intelligent agents to understand the dynamically changing world. However, designing an effective 4D backbone remains challenging, mainly due to the irregular and unordered distribution of points and temporal inconsistencies across frames. Also, recent transformer-based 4D backbones commonly suffer from large computational costs due to their quadratic complexity, particularly for long video sequences. To address these challenges, we propose a novel point cloud video understanding backbone purely based on the State Space Models (SSMs). Specifically, we first disentangle space and time in 4D video sequences and then establish the spatio-temporal correlation with the unified spatial-temporal Mamba blocks. The Intra-frame Spatial Mamba module is developed to encode locally similar geometric structures within a certain temporal stride. Subsequently, locally correlated tokens are delivered to the Inter-frame Temporal Mamba module, which integrates long-term point features across the entire video with linear complexity. Our proposed Mamba4D achieves competitive performance on the MSR-Action3D action recognition (+10.4% accuracy), HOI4D action segmentation (+0.7 F1 Score), and Synthia4D semantic segmentation (+0.19 mIoU) datasets. Mamba4D also has a significant efficiency improvement, especially for long video sequences, with 87.5% GPU memory reduction and 5.36 times speed-up. Codes are released at https://github.com/IRMVLab/Mamba4D.
|
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] |
442 |
Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentation
|
[
"Xin Zhang",
"Robby T. Tan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Mamba_as_a_Bridge_Where_Vision_Foundation_Models_Meet_Vision_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Mamba_as_a_Bridge_Where_Vision_Foundation_Models_Meet_Vision_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Mamba_as_a_CVPR_2025_supplemental.pdf
|
2504.03193
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Xin and Tan, Robby T.},
title = {Mamba as a Bridge: Where Vision Foundation Models Meet Vision Language Models for Domain-Generalized Semantic Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14527-14537}
}
|
Vision Foundation Models (VFMs) and Vision-Language Models (VLMs) have gained traction in Domain Generalized Semantic Segmentation (DGSS) due to their strong generalization capabilities. However, existing DGSS methods often rely exclusively on either VFMs or VLMs, overlooking their complementary strengths. VFMs (e.g., DINOv2) excel at capturing fine-grained features, while VLMs (e.g., CLIP) provide robust text alignment but struggle with coarse granularity. Despite their complementary strengths, effectively integrating VFMs and VLMs with attention mechanisms is challenging, as the increased patch tokens complicate long-sequence modeling. To address this, we propose MFuser, a novel Mamba-based fusion framework that efficiently combines the strengths of VFMs and VLMs while maintaining linear scalability in sequence length. MFuser consists of two key components: MVFuser, which acts as a co-adapter to jointly fine-tune the two models by capturing both sequential and spatial dynamics; and MTEnhancer, a hybrid attention-Mamba module that refines text embeddings by incorporating image priors. Our approach achieves precise feature locality and strong text alignment without incurring significant computational overhead. Extensive experiments demonstrate that MFuser significantly outperforms state-of-the-art DGSS methods, achieving 68.20 mIoU on synthetic-to-real and 71.87 mIoU on real-to-real benchmarks. The code is available at https://github.com/devinxzhang/MFuser.
|
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] |
443 |
Vision-Guided Action: Enhancing 3D Human Motion Prediction with Gaze-informed Affordance in 3D Scenes
|
[
"Ting Yu",
"Yi Lin",
"Jun Yu",
"Zhenyu Lou",
"Qiongjie Cui"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Vision-Guided_Action_Enhancing_3D_Human_Motion_Prediction_with_Gaze-informed_Affordance_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Vision-Guided_Action_Enhancing_3D_Human_Motion_Prediction_with_Gaze-informed_Affordance_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Vision-Guided_Action_Enhancing_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Yu_2025_CVPR,
author = {Yu, Ting and Lin, Yi and Yu, Jun and Lou, Zhenyu and Cui, Qiongjie},
title = {Vision-Guided Action: Enhancing 3D Human Motion Prediction with Gaze-informed Affordance in 3D Scenes},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12335-12346}
}
|
Recent advances in human motion prediction (HMP) have shifted focus from isolated motion data to integrating human-scene correlations. In particular, the latest methods leverage human gaze points, using their spatial coordinates to indicate intent--where a person might move within a 3D environment. Despite promising trajectory results, these methods often produce inaccurate poses by overlooking the semantic implications of gaze, specifically the affordances of observed objects, which indicate the possible interactions. To address this, we propose GAP3DS, an affordance-aware HMP model that utilizes gaze-informed object affordances to improve HMP in complex 3D environments. GAP3DS incorporates a gaze-guided affordance learner to identify relevant objects in the scene and infer their affordances based on human gaze, thus contextualizing future human-object interactions. This affordance information, enriched with visual features and gaze data, conditions the generation of multiple human-object interaction poses, which are subsequently decoded into final motion predictions. Extensive experiments on two real-world datasets demonstrate that GAP3DS outperforms state-of-the-art methods in both trajectory and pose accuracy, producing more physically consistent and contextually grounded predictions. For more details and code, please refer to the project page.
|
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] |
444 |
LOGICZSL: Exploring Logic-induced Representation for Compositional Zero-shot Learning
|
[
"Peng Wu",
"Xiankai Lu",
"Hao Hu",
"Yongqin Xian",
"Jianbing Shen",
"Wenguan Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_LOGICZSL_Exploring_Logic-induced_Representation_for_Compositional_Zero-shot_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_LOGICZSL_Exploring_Logic-induced_Representation_for_Compositional_Zero-shot_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_LOGICZSL_Exploring_Logic-induced_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wu_2025_CVPR,
author = {Wu, Peng and Lu, Xiankai and Hu, Hao and Xian, Yongqin and Shen, Jianbing and Wang, Wenguan},
title = {LOGICZSL: Exploring Logic-induced Representation for Compositional Zero-shot Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {30301-30311}
}
|
Compositional zero-shot learning (CZSL) aims to recognize unseen attribute-object compositions by learning the primitive concepts (*i.e.*, attribute and object) from the training set. While recent works achieve impressive results in CZSL by leveraging large vision-language models like CLIP, they ignore the rich semantic relationships between primitive concepts and their compositions. In this work, we propose LOGICZSL, a novel logic-induced learning framework to explicitly model the semantic relationships. Our logic-induced learning framework formulates the relational knowledge constructed from large language models as a set of logic rules, and grounds them onto the training data. Our logic-induced losses are complementary to the widely used CZSL losses, therefore can be employed to inject the semantic information into any existing CZSL methods. Extensive experimental results show that our method brings significant performance improvements across diverse datasets (*i.e.*, CGQA, UT-Zappos50K, MIT-States) with strong CLIP-based methods and settings (*i.e.*, Close World, Open World).
|
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] |
445 |
ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation
|
[
"Ling-An Zeng",
"Guohong Huang",
"Yi-Lin Wei",
"Shengbo Gu",
"Yu-Ming Tang",
"Jingke Meng",
"Wei-Shi Zheng"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_ChainHOI_Joint-based_Kinematic_Chain_Modeling_for_Human-Object_Interaction_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_ChainHOI_Joint-based_Kinematic_Chain_Modeling_for_Human-Object_Interaction_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeng_ChainHOI_Joint-based_Kinematic_CVPR_2025_supplemental.zip
|
2503.13130
|
@InProceedings{Zeng_2025_CVPR,
author = {Zeng, Ling-An and Huang, Guohong and Wei, Yi-Lin and Gu, Shengbo and Tang, Yu-Ming and Meng, Jingke and Zheng, Wei-Shi},
title = {ChainHOI: Joint-based Kinematic Chain Modeling for Human-Object Interaction Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {12358-12369}
}
|
We propose ChainHOI, a novel approach for text-driven human-object interaction (HOI) generation that explicitly models interactions at both the joint and kinetic chain levels. Unlike existing methods that implicitly model interactions using full-body poses as tokens, we argue that explicitly modeling joint-level interactions is more natural and effective for generating realistic HOIs, as it directly captures the geometric and semantic relationships between joints, rather than modeling interactions in the latent pose space. To this end, ChainHOI introduces a novel joint graph to capture potential interactions with objects, and a Generative Spatiotemporal Graph Convolution Network to explicitly model interactions at the joint level. Furthermore, we propose a Kinematics-based Interaction Module that explicitly models interactions at the kinetic chain level, ensuring more realistic and biomechanically coherent motions. Evaluations on two public datasets demonstrate that ChainHOI significantly outperforms previous methods, generating more realistic, and semantically consistent HOIs.
|
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] |
446 |
CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss
|
[
"Dileepa Pitawela",
"Gustavo Carneiro",
"Hsiang-Ting Chen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Pitawela_CLOC_Contrastive_Learning_for_Ordinal_Classification_with_Multi-Margin_N-pair_Loss_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Pitawela_CLOC_Contrastive_Learning_for_Ordinal_Classification_with_Multi-Margin_N-pair_Loss_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pitawela_CLOC_Contrastive_Learning_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Pitawela_2025_CVPR,
author = {Pitawela, Dileepa and Carneiro, Gustavo and Chen, Hsiang-Ting},
title = {CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15538-15548}
}
|
In ordinal classification, misclassifying neighboring ranks is common, yet the consequences of these errors are not the same.For example, misclassifying benign tumor categories is less consequential, compared to an error at the pre-cancerous to cancerous threshold, which could profoundly influence treatment choices. Despite this, existing ordinal classification methods do not account for the varying importance of these margins, treating all neighboring classes as equally significant. To address this limitation, we propose CLOC, a new margin-based contrastive learning method for ordinal classification that learns an ordered representation based on the optimization of multiple margins with a novel multi-margin n-pair loss (MMNP).CLOC enables flexible decision boundaries across key adjacent categories, facilitating smooth transitions between classes and reducing the risk of overfitting to biases present in the training data.We provide empirical discussion regarding the properties of MMNP and show experimental results on five real-world image datasets (Adience, Historical Colour Image Dating, Knee Osteoarthritis, Indian Diabetic Retinopathy Image, and Breast Carcinoma Subtyping) and one synthetic dataset simulating clinical decision bias.Our results demonstrate that CLOC outperforms existing ordinal classification methods and show the interpretability and controllability of CLOC in learning meaningful, ordered representations that align with clinical and practical needs.
|
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] |
447 |
Universal Actions for Enhanced Embodied Foundation Models
|
[
"Jinliang Zheng",
"Jianxiong Li",
"Dongxiu Liu",
"Yinan Zheng",
"Zhihao Wang",
"Zhonghong Ou",
"Yu Liu",
"Jingjing Liu",
"Ya-Qin Zhang",
"Xianyuan Zhan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Universal_Actions_for_Enhanced_Embodied_Foundation_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Universal_Actions_for_Enhanced_Embodied_Foundation_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Universal_Actions_for_CVPR_2025_supplemental.pdf
|
2501.10105
|
@InProceedings{Zheng_2025_CVPR,
author = {Zheng, Jinliang and Li, Jianxiong and Liu, Dongxiu and Zheng, Yinan and Wang, Zhihao and Ou, Zhonghong and Liu, Yu and Liu, Jingjing and Zhang, Ya-Qin and Zhan, Xianyuan},
title = {Universal Actions for Enhanced Embodied Foundation Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22508-22519}
}
|
Training on diverse, internet-scale data is a key factor in the success of recent large foundation models. Yet, using the same recipe for building embodied agents has faced noticeable difficulties. Despite the availability of many crowd-sourced embodied datasets, their action spaces often exhibit significant heterogeneity due to distinct physical embodiment and control interfaces for different robots, causing substantial challenges in developing embodied foundation models using cross-embodiment data. In this paper, we introduce UniAct, a new embodied foundation modeling framework operating in the Universal Action Space. Our learned universal actions capture the generic behaviors across diverse robots by exploiting their shared structural features, and enable enhanced cross-domain data utilization and cross-embodiment generalizations by eliminating the notorious heterogeneity. Moreover, the universal actions can be efficiently translated back to heterogeneous actionable commands by simply adding embodiment-specific details, from which fast adaptation to new robots becomes simple and straightforward. Our 0.5B instantiation of UniAct outperforms 14X larger SOTA embodied foundation models in extensive evaluations on various real-world and simulation robots, showcasing exceptional cross-embodiment control and adaptation capability, highlighting the crucial benefit of adopting universal actions.
|
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448 |
ObjectMover: Generative Object Movement with Video Prior
|
[
"Xin Yu",
"Tianyu Wang",
"Soo Ye Kim",
"Paul Guerrero",
"Xi Chen",
"Qing Liu",
"Zhe Lin",
"Xiaojuan Qi"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_ObjectMover_Generative_Object_Movement_with_Video_Prior_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_ObjectMover_Generative_Object_Movement_with_Video_Prior_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_ObjectMover_Generative_Object_CVPR_2025_supplemental.pdf
|
2503.08037
|
@InProceedings{Yu_2025_CVPR,
author = {Yu, Xin and Wang, Tianyu and Kim, Soo Ye and Guerrero, Paul and Chen, Xi and Liu, Qing and Lin, Zhe and Qi, Xiaojuan},
title = {ObjectMover: Generative Object Movement with Video Prior},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17682-17691}
}
|
Simple as it seems, moving an object to another location within an image is, in fact, a challenging image-editing task that requires re-harmonizing the lighting, adjusting the pose based on perspective, accurately filling occluded regions, and ensuring coherent synchronization of shadows and reflections while maintaining the object identity. In this paper, we present ObjectMover, a generative model that can perform object movement in highly challenging scenes. Our key insight is that we model this task as a sequence-to-sequence problem and fine-tune a video generation model to leverage its knowledge of consistent object generation across video frames. We show that with this approach, our model is able to adjust to complex real-world scenarios, handling extreme lighting harmonization and object effect movement. As large-scale data for object movement are unavailable, we construct a data generation pipeline using a modern game engine to synthesize high-quality data pairs. We further propose a multi-task learning strategy that enables training on real-world video data to improve the model generalization. Through extensive experiments, we demonstrate that ObjectMover achieves outstanding results and adapts well to real-world scenarios.
|
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] |
449 |
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution
|
[
"Junyang Chen",
"Jinshan Pan",
"Jiangxin Dong"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_FaithDiff_Unleashing_Diffusion_Priors_for_Faithful_Image_Super-resolution_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_FaithDiff_Unleashing_Diffusion_Priors_for_Faithful_Image_Super-resolution_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_FaithDiff_Unleashing_Diffusion_CVPR_2025_supplemental.pdf
|
2411.18824
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Junyang and Pan, Jinshan and Dong, Jiangxin},
title = {FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {28188-28197}
}
|
Faithful image super-resolution (SR) not only needs to recover images that appear realistic, similar to image generation tasks, but also requires that the restored images maintain fidelity and structural consistency with the input. To this end, we propose a simple and effective method, named FaithDiff, to fully harness the impressive power of latent diffusion models (LDMs) for faithful image SR. In contrast to existing diffusion-based SR methods that freeze the diffusion model pre-trained on high-quality images, we propose to unleash the diffusion prior to identify useful information and recover faithful structures. As there exists a significant gap between the features of degraded inputs and the noisy latent from the diffusion model, we then develop an effective alignment module to explore useful features from degraded inputs to align well with the diffusion process. Considering the indispensable roles and interplay of the encoder and diffusion model in LDMs, we jointly fine-tune them in a unified optimization framework, facilitating the encoder to extract useful features that coincide with diffusion process. Extensive experimental results demonstrate that FaithDiff outperforms state-of-the-art methods, providing high-quality and faithful SR results.
|
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] |
450 |
MLLM-as-a-Judge for Image Safety without Human Labeling
|
[
"Zhenting Wang",
"Shuming Hu",
"Shiyu Zhao",
"Xiaowen Lin",
"Felix Juefei-Xu",
"Zhuowei Li",
"Ligong Han",
"Harihar Subramanyam",
"Li Chen",
"Jianfa Chen",
"Nan Jiang",
"Lingjuan Lyu",
"Shiqing Ma",
"Dimitris N. Metaxas",
"Ankit Jain"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MLLM-as-a-Judge_for_Image_Safety_without_Human_Labeling_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MLLM-as-a-Judge_for_Image_Safety_without_Human_Labeling_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MLLM-as-a-Judge_for_Image_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Zhenting and Hu, Shuming and Zhao, Shiyu and Lin, Xiaowen and Juefei-Xu, Felix and Li, Zhuowei and Han, Ligong and Subramanyam, Harihar and Chen, Li and Chen, Jianfa and Jiang, Nan and Lyu, Lingjuan and Ma, Shiqing and Metaxas, Dimitris N. and Jain, Ankit},
title = {MLLM-as-a-Judge for Image Safety without Human Labeling},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14657-14666}
}
|
Image content safety has become a significant challenge with the rise of visual media on online platforms. Meanwhile, in the age of AI-generated content (AIGC), many image generation models are capable of producing harmful content, such as images containing sexual or violent material. Thus, it becomes crucial to identify such unsafe images based on established safety rules. Pre-trained Multimodal Large Language Models (MLLMs) offer potential in this regard, given their strong pattern recognition abilities. Existing approaches typically fine-tune MLLMs with human-labeled datasets, which however brings a series of drawbacks. First, relying on human annotators to label data following intricate and detailed guidelines is both expensive and labor-intensive. Furthermore, users of safety judgment systems may need to frequently update safety rules, making fine-tuning on human-based annotation more challenging. This raises the research question: Can we detect unsafe images by querying MLLMs in a zero-shot setting using a predefined safety constitution (a set of safety rules)? Our research showed that simply querying pre-trained MLLMs does not yield satisfactory results. This lack of effectiveness stems from factors such as the subjectivity of safety rules, the complexity of lengthy constitutions, and the inherent biases in the models. To address these challenges, we propose a MLLM-based method includes objectifying safety rules, assessing the relevance between rules and images, making quick judgments based on debiased token probabilities with logically complete yet simplified precondition chains for safety rules, and conducting more in-depth reasoning with cascaded chain-of-thought processes if necessary. Experiment results demonstrate that our method is highly effective for zero-shot image safety judgment tasks.
|
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] |
451 |
A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations
|
[
"Théo Bodrito",
"Olivier Flasseur",
"Julien Mairal",
"Jean Ponce",
"Maud Langlois",
"Anne-Marie Lagrange"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Bodrito_A_New_Statistical_Model_of_Star_Speckles_for_Learning_to_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Bodrito_A_New_Statistical_Model_of_Star_Speckles_for_Learning_to_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bodrito_A_New_Statistical_CVPR_2025_supplemental.pdf
|
2503.17117
|
@InProceedings{Bodrito_2025_CVPR,
author = {Bodrito, Th\'eo and Flasseur, Olivier and Mairal, Julien and Ponce, Jean and Langlois, Maud and Lagrange, Anne-Marie},
title = {A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1230-1240}
}
|
The search for exoplanets is an active field in astronomy, with direct imaging as one of the most challenging methods due to faint exoplanet signals buried within stronger residual starlight. Successful detection requires advanced image processing to separate the exoplanet signal from this nuisance component. This paper presents a novel statistical model that captures nuisance fluctuations using a multi-scale approach, leveraging problem symmetries and a joint spectral channel representation grounded in physical principles. Our model integrates into an interpretable, end-to-end learnable framework for simultaneous exoplanet detection and flux estimation. The proposed algorithm is evaluated against the state of the art using datasets from the SPHERE instrument operating at the Very Large Telescope (VLT). It significantly improves the precision-recall trade-off, notably on challenging datasets that are otherwise unusable by astronomers. The proposed approach is computationally efficient, robust to varying data quality, and well suited for large-scale observational surveys.
|
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] |
452 |
Scene-agnostic Pose Regression for Visual Localization
|
[
"Junwei Zheng",
"Ruiping Liu",
"Yufan Chen",
"Zhenfang Chen",
"Kailun Yang",
"Jiaming Zhang",
"Rainer Stiefelhagen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Scene-agnostic_Pose_Regression_for_Visual_Localization_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Scene-agnostic_Pose_Regression_for_Visual_Localization_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Scene-agnostic_Pose_Regression_CVPR_2025_supplemental.pdf
|
2503.19543
|
@InProceedings{Zheng_2025_CVPR,
author = {Zheng, Junwei and Liu, Ruiping and Chen, Yufan and Chen, Zhenfang and Yang, Kailun and Zhang, Jiaming and Stiefelhagen, Rainer},
title = {Scene-agnostic Pose Regression for Visual Localization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27092-27102}
}
|
Absolute Pose Regression (APR) predicts 6D camera poses but lacks the adaptability to unknown environments without retraining, while Relative Pose Regression (RPR) generalizes better yet requires a large image retrieval database. Visual Odometry (VO) generalizes well in unseen environments but suffers from accumulated error in open trajectories. To address this dilemma, we introduce a new task, Scene-agnostic Pose Regression (SPR), which can achieve accurate pose regression in a flexible way while eliminating the need for retraining or databases. To benchmark SPR, we created a large-scale dataset, 360SPR, with over 200K photorealistic panoramas, 3.6M pinhole images and camera poses in 270 scenes at three different sensor heights. Furthermore, a SPR-Mamba model is initially proposed to address SPR in a dual-branch manner. Extensive experiments and studies demonstrate the effectiveness of our SPR paradigm, dataset, and model. In the unknown scenes of both 360SPR and 360Loc datasets, our method consistently outperforms APR, RPR and VO. The dataset and code are available at SPR.
|
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] |
453 |
Learning to Filter Outlier Edges in Global SfM
|
[
"Nicole Damblon",
"Marc Pollefeys",
"Daniel Barath"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Damblon_Learning_to_Filter_Outlier_Edges_in_Global_SfM_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Damblon_Learning_to_Filter_Outlier_Edges_in_Global_SfM_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Damblon_Learning_to_Filter_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Damblon_2025_CVPR,
author = {Damblon, Nicole and Pollefeys, Marc and Barath, Daniel},
title = {Learning to Filter Outlier Edges in Global SfM},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11558-11568}
}
|
We present a novel approach to enhance camera pose estimation in global Structure-from-Motion (SfM) frameworks by filtering inaccurate pose graph edges - representing relative translation estimates - before applying translation averaging. In SfM, pose graph vertices represent images, and edges represent relative poses (rotations and translations) between cameras. We reformulate the edge filtering problem as a vertex filtering in the dual graph, specifically, a line graph where vertices correspond to edges in the original graph and edges correspond to cameras. Utilizing this representation, we frame the problem as a binary classification over nodes in the dual graph. To identify outlier edges, we employ a Transformer-based architecture. To overcome the challenge of memory overflow caused by converting to a line graph, we introduce a clustering-based graph processing approach, enabling our method to be applied to arbitrarily large pose graphs. Our method outperforms existing relative translation filtering techniques in terms of camera position accuracy and can be seamlessly integrated with other filters. The code is available at https://github.com/DmblnNicole/LFOE-GlobalSfM.
|
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0.03280971199274063,
0.014601543545722961
] |
454 |
Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification
|
[
"Gaozheng Pei",
"Shaojie Lyu",
"Gong Chen",
"Ke Ma",
"Qianqian Xu",
"Yingfei Sun",
"Qingming Huang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Pei_Divide_and_Conquer_Heterogeneous_Noise_Integration_for_Diffusion-based_Adversarial_Purification_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Pei_Divide_and_Conquer_Heterogeneous_Noise_Integration_for_Diffusion-based_Adversarial_Purification_CVPR_2025_paper.pdf
| null |
2503.01407
|
@InProceedings{Pei_2025_CVPR,
author = {Pei, Gaozheng and Lyu, Shaojie and Chen, Gong and Ma, Ke and Xu, Qianqian and Sun, Yingfei and Huang, Qingming},
title = {Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {29268-29277}
}
|
Existing diffusion-based purification methods aim to disrupt adversarial perturbations by introducing a certain amount of noise through a forward diffusion process, followed by a reverse process to recover clean examples. However, this approach is fundamentally flawed: the uniform operation of the forward process across all pixels compromises normal pixels while attempting to combat adversarial perturbations, resulting in the target model producing incorrect predictions. Simply relying on low-intensity noise is insufficient for effective defense. To address this critical issue, we implement a heterogeneous purification strategy grounded in the interpretability of neural networks. Our method decisively applies higher-intensity noise to specific pixels that the target model focuses on while the remaining pixels are subjected to only low-intensity noise. This requirement motivates us to redesign the sampling process of the diffusion model, allowing for the effective removal of varying noise levels. Furthermore, to evaluate our method against strong adaptative attack, our proposed method sharply reduces time cost and memory usage through a single-step resampling. The empirical evidence from extensive experiments across three datasets demonstrates that our method outperforms most current adversarial training and purification techniques by a substantial margin.
|
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] |
455 |
SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning
|
[
"Ye Liu",
"Meng Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_SEC-PromptSEmantic_Complementary_Prompting_for_Few-Shot_Class-Incremental_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_SEC-PromptSEmantic_Complementary_Prompting_for_Few-Shot_Class-Incremental_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_SEC-PromptSEmantic_Complementary_Prompting_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Liu_2025_CVPR,
author = {Liu, Ye and Yang, Meng},
title = {SEC-Prompt:SEmantic Complementary Prompting for Few-Shot Class-Incremental Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25643-25656}
}
|
Few-shot class-incremental learning (FSCIL) presents a significant challenge in machine learning, requiring models to integrate new classes from limited examples while preserving performance on previously learned classes. Recently, prompt-based CIL approaches leverage ample data to train prompts, effectively mitigating catastrophic forgetting. However, these methods do not account for the semantic features embedded in prompts, exacerbating the plasticity-stability dilemma in few-shot incremental learning. In this paper, we propose a novel and simple framework named SEmantic Complementary Prompt(SEC-Prompt), which learns two sets of semantically complementary prompts based on an adaptive query: discriminative prompts(D-Prompt) and non-discriminative prompts(ND-Prompt). D-Prompt enhances the separation of class-specific feature distributions by strengthening key discriminative features, while ND-Prompt balances non-discriminative information to promote generalization to novel classes. To efficiently learn high-quality knowledge from limited samples, we leverage ND-Prompt for data augmentation to increase sample diversity and introduce Prompt Clustering Loss to prevent noise contamination in D-Prompt, ensuring robust discriminative feature learning and improved generalization. Our experimental results showcase state-of-the-art performance across three benchmark datasets, including CIFAR100, ImageNet-R and CUB datasets.
|
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] |
456 |
LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes
|
[
"Xiang Xu",
"Lingdong Kong",
"Hui Shuai",
"Liang Pan",
"Ziwei Liu",
"Qingshan Liu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_LiMoE_Mixture_of_LiDAR_Representation_Learners_from_Automotive_Scenes_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_LiMoE_Mixture_of_LiDAR_Representation_Learners_from_Automotive_Scenes_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_LiMoE_Mixture_of_CVPR_2025_supplemental.pdf
|
2501.04004
|
@InProceedings{Xu_2025_CVPR,
author = {Xu, Xiang and Kong, Lingdong and Shuai, Hui and Pan, Liang and Liu, Ziwei and Liu, Qingshan},
title = {LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27368-27379}
}
|
LiDAR data pretraining offers a promising approach to leveraging large-scale, readily available datasets for enhanced data utilization. However, existing methods predominantly focus on sparse voxel representation, overlooking the complementary attributes provided by other LiDAR representations. In this work, we propose LiMoE, a framework that integrates the Mixture of Experts (MoE) paradigm into LiDAR data representation learning to synergistically combine multiple representations, such as range images, sparse voxels, and raw points. Our approach consists of three stages: i) Image-to-LiDAR Pretraining, which transfers prior knowledge from images to point clouds across different representations; ii) Contrastive Mixture Learning (CML), which uses MoE to adaptively activate relevant attributes from each representation and distills these mixed features into a unified 3D network; iii) Semantic Mixture Supervision (SMS), which combines semantic logits from multiple representations to boost downstream segmentation performance. Extensive experiments across eleven large-scale LiDAR datasets demonstrate our effectiveness and superiority. The code has been made publicly accessible.
|
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] |
457 |
CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
|
[
"Qingqing Zhao",
"Yao Lu",
"Moo Jin Kim",
"Zipeng Fu",
"Zhuoyang Zhang",
"Yecheng Wu",
"Zhaoshuo Li",
"Qianli Ma",
"Song Han",
"Chelsea Finn",
"Ankur Handa",
"Tsung-Yi Lin",
"Gordon Wetzstein",
"Ming-Yu Liu",
"Donglai Xiang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_CoT-VLA_Visual_Chain-of-Thought_Reasoning_for_Vision-Language-Action_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_CoT-VLA_Visual_Chain-of-Thought_Reasoning_for_Vision-Language-Action_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_CoT-VLA_Visual_Chain-of-Thought_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Zhao_2025_CVPR,
author = {Zhao, Qingqing and Lu, Yao and Kim, Moo Jin and Fu, Zipeng and Zhang, Zhuoyang and Wu, Yecheng and Li, Zhaoshuo and Ma, Qianli and Han, Song and Finn, Chelsea and Handa, Ankur and Lin, Tsung-Yi and Wetzstein, Gordon and Liu, Ming-Yu and Xiang, Donglai},
title = {CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1702-1713}
}
|
Vision-language-action models (VLAs) have shown potential in leveraging pretrained vision-language models and diverse robot demonstrations for learning generalizable sensorimotor control. While this paradigm effectively utilizes large-scale data from both robotic and non-robotic sources, current VLAs primarily focus on direct input--output mappings, lacking the intermediate reasoning steps crucial for complex manipulation tasks. As a result, existing VLAs lack temporal planning or reasoning capabilities. In this paper, we introduce a method that incorporates explicit visual chain-of-thought (CoT) reasoning into vision-language-action models (VLAs) by predicting future image frames autoregressively as visual goals before generating a short action sequence to achieve these goals. We introduce CoT-VLA, a state-of-the-art 7B VLA that can understand and generate visual and action tokens. Our experimental results demonstrate that CoT-VLA achieves strong performance, outperforming the state-of-the-art VLA model by 17% in real-world manipulation tasks and 6% in simulation benchmarks. Videos are available at: https://cot-vla.github.io/.
|
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] |
458 |
WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models
|
[
"Fu Feng",
"Yucheng Xie",
"Jing Wang",
"Xin Geng"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_WAVE_Weight_Templates_for_Adaptive_Initialization_of_Variable-sized_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_WAVE_Weight_Templates_for_Adaptive_Initialization_of_Variable-sized_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_WAVE_Weight_Templates_CVPR_2025_supplemental.pdf
|
2406.17503
|
@InProceedings{Feng_2025_CVPR,
author = {Feng, Fu and Xie, Yucheng and Wang, Jing and Geng, Xin},
title = {WAVE: Weight Templates for Adaptive Initialization of Variable-sized Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {4819-4828}
}
|
The growing complexity of model parameters underscores the significance of pre-trained models. However, deployment constraints often necessitate models of varying sizes, exposing limitations in the conventional pre-training and fine-tuning paradigm, particularly when target model sizes are incompatible with pre-trained ones. To address this challenge, we propose WAVE, a novel approach that reformulates variable-sized model initialization from a multi-task perspective, where initializing each model size is treated as a distinct task. WAVE employs shared, size-agnostic weight templates alongside size-specific weight scalers to achieve consistent initialization across various model sizes. These weight templates, constructed within the Learngene framework, integrate knowledge from pre-trained models through a distillation process constrained by Kronecker-based rules. Target models are then initialized by concatenating and weighting these templates, with adaptive connection rules established by lightweight weight scalers, whose parameters are learned from minimal training data. Extensive experiments demonstrate the efficiency of WAVE, achieving state-of-the-art performance in initializing models of various depth and width. The knowledge encapsulated in weight templates is also task-agnostic, allowing for seamless transfer across diverse downstream datasets. Code will be made available at https://github.com/fu-feng/WAVE.
|
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] |
459 |
Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection
|
[
"Xinjie Cui",
"Yuezun Li",
"Ao Luo",
"Jiaran Zhou",
"Junyu Dong"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cui_Forensics_Adapter_Adapting_CLIP_for_Generalizable_Face_Forgery_Detection_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cui_Forensics_Adapter_Adapting_CLIP_for_Generalizable_Face_Forgery_Detection_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Cui_2025_CVPR,
author = {Cui, Xinjie and Li, Yuezun and Luo, Ao and Zhou, Jiaran and Dong, Junyu},
title = {Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19207-19217}
}
|
We describe the Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector. Although CLIP is highly versatile, adapting it for face forgery detection is non-trivial as forgery-related knowledge is entangled with a wide range of unrelated knowledge. Existing methods treat CLIP merely as a feature extractor, lacking task-specific adaptation, which limits their effectiveness. To address this, we introduce an adapter to learn face forgery traces -- the blending boundaries unique to forged faces, guided by task-specific objectives. Then we enhance the CLIP visual tokens with a dedicated interaction strategy that communicates knowledge across CLIP and the adapter. Since the adapter is alongside CLIP, its versatility is highly retained, naturally ensuring strong generalizability in face forgery detection. With only 5.7M trainable parameters, our method achieves a significant performance boost, improving by approximately 7% on average across five standard datasets. We believe the proposed method can serve as a baseline for future CLIP-based face forgery detection methods.
|
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] |
460 |
KAC: Kolmogorov-Arnold Classifier for Continual Learning
|
[
"Yusong Hu",
"Zichen Liang",
"Fei Yang",
"Qibin Hou",
"Xialei Liu",
"Ming-Ming Cheng"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_KAC_Kolmogorov-Arnold_Classifier_for_Continual_Learning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_KAC_Kolmogorov-Arnold_Classifier_for_Continual_Learning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_KAC_Kolmogorov-Arnold_Classifier_CVPR_2025_supplemental.pdf
|
2503.21076
|
@InProceedings{Hu_2025_CVPR,
author = {Hu, Yusong and Liang, Zichen and Yang, Fei and Hou, Qibin and Liu, Xialei and Cheng, Ming-Ming},
title = {KAC: Kolmogorov-Arnold Classifier for Continual Learning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {15297-15307}
}
|
Continual learning requires models to train continuously across consecutive tasks without forgetting. Most existing methods utilize linear classifiers, which struggle to maintain a stable classification space while learning new tasks. Inspired by the success of Kolmogorov-Arnold Networks (KAN) in preserving learning stability during simple continual regression tasks, we set out to explore their potential in more complex continual learning scenarios. In this paper, we introduce the Kolmogorov-Arnold Classifier (KAC), a novel classifier developed for continual learning based on the KAN structure. We delve into the impact of KAN's spline functions and introduce Radial Basis Functions (RBF) for improved compatibility with continual learning. We replace linear classifiers with KAC in several recent approaches and conduct experiments across various continual learning benchmarks, all of which demonstrate performance improvements, highlighting the effectiveness and robustness of KAC in continual learning.
|
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461 |
PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing
|
[
"Ziyu Wu",
"Yufan Xiong",
"Mengting Niu",
"Fangting Xie",
"Quan Wan",
"Qijun Ying",
"Boyan Liu",
"Xiaohui Cai"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_PI-HMR_Towards_Robust_In-bed_Temporal_Human_Shape_Reconstruction_with_Contact_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_PI-HMR_Towards_Robust_In-bed_Temporal_Human_Shape_Reconstruction_with_Contact_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_PI-HMR_Towards_Robust_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wu_2025_CVPR,
author = {Wu, Ziyu and Xiong, Yufan and Niu, Mengting and Xie, Fangting and Wan, Quan and Ying, Qijun and Liu, Boyan and Cai, Xiaohui},
title = {PI-HMR: Towards Robust In-bed Temporal Human Shape Reconstruction with Contact Pressure Sensing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27739-27749}
}
|
Long-term in-bed monitoring benefits automatic and real-time health management within healthcare, and the advancement of human shape reconstruction technologies further enhances the representation and visualization of users' activity patterns. However, existing technologies are primarily based on visual cues, facing serious challenges in non-light-of-sight and privacy-sensitive in-bed scenes. Pressure-sensing bedsheets offer a promising solution for real-time motion reconstruction. Yet, limited exploration in model designs and data have hindered its further development. To tackle these issues, we propose a general framework that bridges gaps in data annotation and model design. Firstly, we introduce SMPLify-IB, an optimization method that overcomes the depth ambiguity issue in top-view scenarios through gravity constraints, enabling generating high-quality 3D human shape annotations for in-bed datasets. Then we present PI-HMR, a temporal-based human shape estimator to regress meshes from pressure sequences. By integrating multi-scale feature fusion with high-pressure distribution and spatial position priors, PI-HMR outperforms SOTA methods with 17.01mm Mean-Per-Joint-Error decrease. This work provides a whole tool-chain to support the development of in-bed monitoring with pressure contact sensing.
|
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] |
462 |
BOOTPLACE: Bootstrapped Object Placement with Detection Transformers
|
[
"Hang Zhou",
"Xinxin Zuo",
"Rui Ma",
"Li Cheng"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_BOOTPLACE_Bootstrapped_Object_Placement_with_Detection_Transformers_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_BOOTPLACE_Bootstrapped_Object_Placement_with_Detection_Transformers_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_BOOTPLACE_Bootstrapped_Object_CVPR_2025_supplemental.pdf
|
2503.21991
|
@InProceedings{Zhou_2025_CVPR,
author = {Zhou, Hang and Zuo, Xinxin and Ma, Rui and Cheng, Li},
title = {BOOTPLACE: Bootstrapped Object Placement with Detection Transformers},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19294-19303}
}
|
In this paper, we tackle the copy-paste image-to-image composition problem with a focus on object placement learning. Prior methods have leveraged generative models to reduce the reliance for dense supervision. However, this often limits their capacity to model complex data distributions. Alternatively, transformer networks with a sparse contrastive loss have been explored, but their over-relaxed regularization often leads to imprecise object placement. We introduce BOOTPLACE, a novel paradigm that formulates object placement as a placement-by-detection problem. Our approach begins by identifying suitable regions of interest for object placement. This is achieved by training a specialized detection transformer on object-subtracted backgrounds, enhanced with multi-object supervisions. It then semantically associates each target compositing object with detected regions based on their complementary characteristics. Through a boostrapped training approach applied to randomly object-subtracted images, our model enforces meaningful placements through extensive paired data augmentation. Experimental results on established benchmarks demonstrate BOOTPLACE's superior performance in object repositioning, markedly surpassing state-of-the-art baselines on Cityscapes and OPA datasets with notable improvements in IOU scores. Additional ablation studies further showcase the compositionality and generalizability of our approach, supported by user study evaluations. Code is available at https://github.com/RyanHangZhou/BOOTPLACE
|
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] |
463 |
CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation
|
[
"Yuxing Long",
"Jiyao Zhang",
"Mingjie Pan",
"Tianshu Wu",
"Taewhan Kim",
"Hao Dong"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Long_CheckManual_A_New_Challenge_and_Benchmark_for_Manual-based_Appliance_Manipulation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Long_CheckManual_A_New_Challenge_and_Benchmark_for_Manual-based_Appliance_Manipulation_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Long_2025_CVPR,
author = {Long, Yuxing and Zhang, Jiyao and Pan, Mingjie and Wu, Tianshu and Kim, Taewhan and Dong, Hao},
title = {CheckManual: A New Challenge and Benchmark for Manual-based Appliance Manipulation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22595-22604}
}
|
Correct use of electrical appliances has significantly improved human life quality. Unlike simple tools that can be manipulated with common sense, different parts of electrical appliances have specific functions defined by manufacturers. If we want the robot to heat bread by microwave, we should enable them to review the microwave's manual first. From the manual, it can learn about component functions, interaction methods, and representative task steps about appliances. However, previous manual-related works remain limited to question-answering tasks while existing manipulation researchers ignore the manual's important role and fail to comprehend multi-page manuals. In this paper, we propose the first manual-based appliance manipulation benchmark CheckManual. Specifically, we design a large model-assisted human-revised data generation pipeline to create manuals based on CAD appliance models. With these manuals, we establish novel manual-based manipulation challenges, metrics, and simulator environments for model performance evaluation. Furthermore, we propose the first manual-based manipulation planning model ManualPlan to set up a group of baselines for the CheckManual benchmark.
|
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] |
464 |
CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset
|
[
"Xiao Wang",
"Fuling Wang",
"Yuehang Li",
"Qingchuan Ma",
"Shiao Wang",
"Bo Jiang",
"Jin Tang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_CXPMRG-Bench_Pre-training_and_Benchmarking_for_X-ray_Medical_Report_Generation_on_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_CXPMRG-Bench_Pre-training_and_Benchmarking_for_X-ray_Medical_Report_Generation_on_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_CXPMRG-Bench_Pre-training_and_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Xiao and Wang, Fuling and Li, Yuehang and Ma, Qingchuan and Wang, Shiao and Jiang, Bo and Tang, Jin},
title = {CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5123-5133}
}
|
X-ray image-based medical report generation (MRG) is a pivotal area in artificial intelligence that can significantly reduce diagnostic burdens and patient wait times. Despite significant progress, we believe that the task has reached a bottleneck due to the limited benchmark datasets and the existing large models' insufficient capability enhancements in this specialized domain. Specifically, the recently released CheXpert Plus dataset lacks comparative evaluation algorithms and their results, providing only the dataset itself. This situation makes the training, evaluation, and comparison of subsequent algorithms challenging. Thus, we conduct a comprehensive benchmarking of existing mainstream X-ray report generation models and large language models (LLMs), on the CheXpert Plus dataset. We believe that the proposed benchmark can provide a solid comparative basis for subsequent algorithms and serve as a guide for researchers to quickly grasp the state-of-the-art models in this field. More importantly, we propose a large model for the X-ray image report generation using a multi-stage pre-training strategy, including self-supervised autoregressive generation and Xray-report contrastive learning, and supervised fine-tuning. Extensive experimental results indicate that the autoregressive pre-training based on Mamba effectively encodes X-ray images, and the image-text contrastive pre-training further aligns the feature spaces, achieving better experimental results.
|
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] |
465 |
FASTer: Focal token Acquiring-and-Scaling Transformer for Long-term 3D Objection Detection
|
[
"Chenxu Dang",
"ZaiPeng Duan",
"Pei An",
"Xinmin Zhang",
"Xuzhong Hu",
"Jie Ma"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Dang_FASTer_Focal_token_Acquiring-and-Scaling_Transformer_for_Long-term_3D_Objection_Detection_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Dang_FASTer_Focal_token_Acquiring-and-Scaling_Transformer_for_Long-term_3D_Objection_Detection_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dang_FASTer_Focal_token_CVPR_2025_supplemental.pdf
|
2503.01899
|
@InProceedings{Dang_2025_CVPR,
author = {Dang, Chenxu and Duan, ZaiPeng and An, Pei and Zhang, Xinmin and Hu, Xuzhong and Ma, Jie},
title = {FASTer: Focal token Acquiring-and-Scaling Transformer for Long-term 3D Objection Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17029-17038}
}
|
Recent top-performing temporal 3D detectors based on Lidars have increasingly adopted region-based paradigms. They first generate coarse proposals, followed by encoding and fusing regional features. However, indiscriminate sampling and fusion often overlook the varying contributions of individual points and lead to exponentially increased complexity as the number of input frames grows. Moreover, arbitrary result-level concatenation limits the global information extraction. In this paper, we propose a Focal Token Acquring-and-Scaling Transformer (FASTer), which dynamically selects focal tokens and condenses token sequences in an adaptive and lightweight manner. Emphasizing the contribution of individual tokens, we propose a simple but effective Adaptive Scaling mechanism to capture geometric contexts while sifting out focal points. Adaptively storing and processing only focal points in historical frames dramatically reduces the overall complexity. Furthermore, a novel Grouped Hierarchical Fusion strategy is proposed, progressively performing sequence scaling and Intra-Group Fusion operations to facilitate the exchange of global spatial and temporal information. Experiments on the Waymo Open Dataset demonstrate that our FASTer significantly outperforms other state-of-the-art detectors in both performance and efficiency while also exhibiting improved flexibility and robustness. The code is available at https://github.com/MSunDYY/FASTer.git.
|
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] |
466 |
SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection
|
[
"Haochen Li",
"Rui Zhang",
"Hantao Yao",
"Xin Zhang",
"Yifan Hao",
"Xinkai Song",
"Shaohui Peng",
"Yongwei Zhao",
"Chen Zhao",
"Yanjun Wu",
"Ling Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_SEEN-DA_SEmantic_ENtropy_guided_Domain-aware_Attention_for_Domain_Adaptive_Object_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_SEEN-DA_SEmantic_ENtropy_guided_Domain-aware_Attention_for_Domain_Adaptive_Object_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_SEEN-DA_SEmantic_ENtropy_CVPR_2025_supplemental.zip
| null |
@InProceedings{Li_2025_CVPR,
author = {Li, Haochen and Zhang, Rui and Yao, Hantao and Zhang, Xin and Hao, Yifan and Song, Xinkai and Peng, Shaohui and Zhao, Yongwei and Zhao, Chen and Wu, Yanjun and Li, Ling},
title = {SEEN-DA: SEmantic ENtropy guided Domain-aware Attention for Domain Adaptive Object Detection},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25465-25475}
}
|
Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. Traditional works focus on aligning visual features between domains to extract domain-invariant knowledge, and recent VLM-based DAOD methods leverage semantic information provided by the textual encoder to supplement domain-specific features for each domain.However, they overlook the role of semantic information in guiding the learning of visual features that are beneficial for adaptation.To solve the problem, we propose semantic entropy to quantify the semantic information contained in visual features, and design SEmantic ENtropy guided Domain-aware Attention (SEEN-DA) to adaptively refine visual features with the semantic information of two domains.Semantic entropy reflects the importance of features based on semantic information, which can serve as attention to select discriminative visual features and suppress semantically irrelevant redundant information.Guided by semantic entropy, we introduce domain-aware attention modules into the visual encoder in SEEN-DA.It utilizes an inter-domain attention branch to extract domain-invariant features and eliminate redundant information, and an intra-domain attention branch to supplement the domain-specific semantic information discriminative on each domain.Comprehensive experiments validate the effectiveness of SEEN-DA, demonstrating significant improvements in cross-domain object detection performance.
|
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] |
467 |
Event-Equalized Dense Video Captioning
|
[
"Kangyi Wu",
"Pengna Li",
"Jingwen Fu",
"Yizhe Li",
"Yang Wu",
"Yuhan Liu",
"Jinjun Wang",
"Sanping Zhou"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Event-Equalized_Dense_Video_Captioning_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Event-Equalized_Dense_Video_Captioning_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Event-Equalized_Dense_Video_CVPR_2025_supplemental.zip
| null |
@InProceedings{Wu_2025_CVPR,
author = {Wu, Kangyi and Li, Pengna and Fu, Jingwen and Li, Yizhe and Wu, Yang and Liu, Yuhan and Wang, Jinjun and Zhou, Sanping},
title = {Event-Equalized Dense Video Captioning},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8417-8427}
}
|
Dense video captioning aims to localize and caption all events in arbitrary untrimmed videos. Although previous methods have achieved appealing results, they still face the issue of temporal bias, i.e, models tend to focus more on events with certain temporal characteristics. Specifically, 1) the temporal distribution of events in training datasets is uneven. Models trained on these datasets will pay less attention to out-of-distribution events. 2) long-duration events have more frame features than short ones and will attract more attention. To address this, we argue that events, with varying temporal characteristics, should be treated equally when it comes to dense video captioning. Intuitively, different events tend to have distinct visual differences due to varied camera views, backgrounds, or subjects. Inspired by that, we intend to utilize visual features to have an approximate perception of possible events and pay equal attention to them. In this paper, we introduce a simple but effective framework, called Event-Equalized Dense Video Captioning(E^2DVC) to overcome the temporal bias and treat all possible events equally. Specifically, an event perception module(EPM) is proposed to do uneven clustering on visual frame features to generate pseudo-events. We enforce the model's attention to these pseudo-events through the pseudo-event initialization module(PEI). A novel event-enhanced encoder(EEE) is also devised to enhance the model's ability to explore frame-frame and frame-event relationships. Experimental results validate the effectiveness of the proposed methods.
|
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] |
468 |
Geometry-guided Online 3D Video Synthesis with Multi-View Temporal Consistency
|
[
"Hyunho Ha",
"Lei Xiao",
"Christian Richardt",
"Thu Nguyen-Phuoc",
"Changil Kim",
"Min H. Kim",
"Douglas Lanman",
"Numair Khan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Ha_Geometry-guided_Online_3D_Video_Synthesis_with_Multi-View_Temporal_Consistency_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Ha_Geometry-guided_Online_3D_Video_Synthesis_with_Multi-View_Temporal_Consistency_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ha_Geometry-guided_Online_3D_CVPR_2025_supplemental.zip
|
2505.18932
|
@InProceedings{Ha_2025_CVPR,
author = {Ha, Hyunho and Xiao, Lei and Richardt, Christian and Nguyen-Phuoc, Thu and Kim, Changil and Kim, Min H. and Lanman, Douglas and Khan, Numair},
title = {Geometry-guided Online 3D Video Synthesis with Multi-View Temporal Consistency},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {11275-11285}
}
|
We introduce a novel geometry-guided online video view synthesis method with enhanced view and temporal consistency. Traditional approaches achieve high-quality synthesis from dense multi-view camera setups but require significant computational resources. In contrast, selective-input methods reduce this cost but often compromise quality, leading to multi-view and temporal inconsistencies such as flickering artifacts. Our method addresses this challenge to deliver efficient, high-quality novel-view synthesis with view and temporal consistency. The key innovation of our approach lies in using global geometry to guide an image-based rendering pipeline. To accomplish this, we progressively refine depth maps using color difference masks across time. These depth maps are then accumulated through truncated signed distance fields in the synthesized view's image space. This depth representation is view and temporally consistent, and is used to guide a pre-trained blending network that fuses multiple forward-rendered input-view images. Thus, the network is encouraged to output geometrically consistent synthesis results across multiple views and time. Our approach achieves consistent, high-quality video synthesis, while running efficiently in an online manner.
|
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] |
469 |
EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation
|
[
"Daikun Liu",
"Lei Cheng",
"Teng Wang",
"Changyin Sun"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_EDCFlow_Exploring_Temporally_Dense_Difference_Maps_for_Event-based_Optical_Flow_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_EDCFlow_Exploring_Temporally_Dense_Difference_Maps_for_Event-based_Optical_Flow_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_EDCFlow_Exploring_Temporally_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Liu_2025_CVPR,
author = {Liu, Daikun and Cheng, Lei and Wang, Teng and Sun, Changyin},
title = {EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {1984-1993}
}
|
Recent learning-based methods for event-based optical flow estimation utilize cost volumes for pixel matching but suffer from redundant computations and limited scalability to higher resolutions for flow refinement. In this work, we take advantage of the complementarity between temporally dense feature differences of adjacent event frames and cost volume and present a lightweight event-based optical flow network (EDCFlow) to achieve high-quality flow estimation at a higher resolution. Specifically, an attention-based multi-scale temporal feature difference layer is developed to capture diverse motion patterns at high resolution in a computation-efficient manner. An adaptive fusion of high-resolution difference motion features and low-resolution correlation motion features is performed to enhance motion representation and model generalization. Notably, EDCFlow can serve as a plug-and-play refinement module for RAFT-like event-based methods to enhance flow details. Extensive experiments demonstrate that EDCFlow achieves better performance with lower complexity compared to existing methods, offering superior generalization. Codes and models will be available at here.
|
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] |
470 |
Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances
|
[
"Yi Yu",
"Botao Ren",
"Peiyuan Zhang",
"Mingxin Liu",
"Junwei Luo",
"Shaofeng Zhang",
"Feipeng Da",
"Junchi Yan",
"Xue Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Point2RBox-v2_Rethinking_Point-supervised_Oriented_Object_Detection_with_Spatial_Layout_Among_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Point2RBox-v2_Rethinking_Point-supervised_Oriented_Object_Detection_with_Spatial_Layout_Among_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Yu_2025_CVPR,
author = {Yu, Yi and Ren, Botao and Zhang, Peiyuan and Liu, Mingxin and Luo, Junwei and Zhang, Shaofeng and Da, Feipeng and Yan, Junchi and Yang, Xue},
title = {Point2RBox-v2: Rethinking Point-supervised Oriented Object Detection with Spatial Layout Among Instances},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19283-19293}
}
|
With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning OOD from point annotations has gained great attention. In this paper, we rethink this challenging task setting with the layout among instances and present Point2RBox-v2. At the core are three principles: 1) Gaussian overlap loss. It learns an upper bound for each instance by treating objects as 2D Gaussian distributions and minimizing their overlap. 2) Voronoi watershed loss. It learns a lower bound for each instance through watershed on Voronoi tessellation. 3) Consistency loss. It learns the size/rotation variation between two output sets with respect to an input image and its augmented view. Supplemented by a few devised techniques, e.g. edge loss and copy-paste, the detector is further enhanced. To our best knowledge, Point2RBox-v2 is the first approach to explore the spatial layout among instances for learning point-supervised OOD. Our solution is elegant and lightweight, yet it is expected to give a competitive performance especially in densely packed scenes: 62.61%/86.15%/34.71% on DOTA/HRSC/FAIR1M.
|
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] |
471 |
LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions
|
[
"Faridoun Mehri",
"Mahdieh Soleymani Baghshah",
"Mohammad Taher Pilehvar"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Mehri_LibraGrad_Balancing_Gradient_Flow_for_Universally_Better_Vision_Transformer_Attributions_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Mehri_LibraGrad_Balancing_Gradient_Flow_for_Universally_Better_Vision_Transformer_Attributions_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mehri_LibraGrad_Balancing_Gradient_CVPR_2025_supplemental.pdf
|
2411.16760
|
@InProceedings{Mehri_2025_CVPR,
author = {Mehri, Faridoun and Baghshah, Mahdieh Soleymani and Pilehvar, Mohammad Taher},
title = {LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {67-78}
}
|
Why do gradient-based explanations struggle with Transformers, and how can we improve them? We identify gradient flow imbalances in Transformers that violate FullGrad-completeness, a critical property for attribution faithfulness that CNNs naturally possess. To address this issue, we introduce LibraGrad--a theoretically grounded post-hoc approach that corrects gradient imbalances through pruning and scaling of backward paths, without changing the forward pass or adding computational overhead. We evaluate LibraGrad using three metric families: Faithfulness, which quantifies prediction changes under perturbations of the most and least relevant features; Completeness Error, which measures attribution conservation relative to model outputs; and Segmentation AP, which assesses alignment with human perception. Extensive experiments across 8 architectures, 4 model sizes, and 5 datasets show that LibraGrad universally enhances gradient-based methods, outperforming existing white-box methods--including Transformer-specific approaches--across all metrics. We demonstrate superior qualitative results through two complementary evaluations: precise text-prompted region highlighting on CLIP models and accurate class discrimination between co-occurring animals on ImageNet-finetuned models--two settings on which existing methods often struggle. LibraGrad is effective even on the attention-free MLP-Mixer architecture, indicating potential for extension to other modern architectures. Our code is freely available at https://nightmachinery.github.io/LibraGrad/.
|
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] |
472 |
Blind Bitstream-corrupted Video Recovery via Metadata-guided Diffusion Model
|
[
"Shuyun Wang",
"Hu Zhang",
"Xin Shen",
"Dadong Wang",
"Xin Yu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Blind_Bitstream-corrupted_Video_Recovery_via_Metadata-guided_Diffusion_Model_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Blind_Bitstream-corrupted_Video_Recovery_via_Metadata-guided_Diffusion_Model_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Blind_Bitstream-corrupted_Video_CVPR_2025_supplemental.zip
| null |
@InProceedings{Wang_2025_CVPR,
author = {Wang, Shuyun and Zhang, Hu and Shen, Xin and Wang, Dadong and Yu, Xin},
title = {Blind Bitstream-corrupted Video Recovery via Metadata-guided Diffusion Model},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22975-22984}
}
|
Bitstream-corrupted video recovery aims to fill in realistic video content due to bitstream corruption during video storage or transmission. Most existing methods typically assume that the predefined masks of the corrupted regions are known in advance. However, manually annotating these masks is laborious and time-consuming, limiting the applicability of existing methods in real-world scenarios. Therefore, we expect to relax this assumption by defining a new blind video recovery setting where the recovery of corrupted regions does not rely on predefined masks. There are two significant challenges in this setting: (i) without predefined masks, how accurately can a model identify the regions requiring recovery? (ii) how to recover contents from extensive and irregular regions, especially when large portions of frames are severely degraded? To address these challenges, we introduce a Metadata-Guided Diffusion Model, dubbed M-GDM. To enable a diffusion model focusing on the corrupted regions, we leverage intrinsic video metadata as a corruption indicator and design a dual-stream metadata encoder. This encoder first embeds the motion vectors and frame types of a video separately and then merges them into a unified metadata representation. The metadata representation will interact with the corrupted latent feature through cross-attention mechanisms at each diffusion step. Meanwhile, to preserve the intact regions, we propose a prior-driven mask predictor that generates pseudo masks for the corrupted regions by leveraging the metadata prior and diffusion prior. These pseudo masks enable the separation and recombination of intact and recovered regions through hard masking. However, imperfections in pseudo mask predictions and hard masking processes often result in boundary artifacts. Thus, we introduce a post-refinement module that refines the hard-masked outputs, enhancing the consistency between intact and recovered regions. Extensive experiment results validate the effectiveness of our method and demonstrate its superiority in the blind video recovery task.
|
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] |
473 |
Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking
|
[
"Junxi Chen",
"Junhao Dong",
"Xiaohua Xie"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Mind_the_Trojan_Horse_Image_Prompt_Adapter_Enabling_Scalable_and_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Mind_the_Trojan_Horse_Image_Prompt_Adapter_Enabling_Scalable_and_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Mind_the_Trojan_CVPR_2025_supplemental.pdf
|
2504.05838
|
@InProceedings{Chen_2025_CVPR,
author = {Chen, Junxi and Dong, Junhao and Xie, Xiaohua},
title = {Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {23785-23794}
}
|
Recently, the Image Prompt Adapter (IP-Adapter) has been increasingly integrated into text-to-image diffusion models (T2I-DMs) to improve controllability. However, in this paper, we reveal that T2I-DMs equipped with the IP-Adapter (T2I-IP-DMs) enable a new jailbreak attack named the hijacking attack. We demonstrate that, by uploading imperceptible image-space adversarial examples (AEs), the adversary can hijack massive benign users to jailbreak an Image Generation Service (IGS) driven by T2I-IP-DMs and mislead the public to discredit the service provider. Worse still, the IP-Adapter's dependency on open-source image encoders reduces the knowledge required to craft AEs. Extensive experiments verify the technical feasibility of the hijacking attack. In light of the revealed threat, we investigate several existing defenses and explore combining the IP-Adapter with adversarially trained models to overcome existing defenses' limitations. Our code is available at https://github.com/fhdnskfbeuv/attackIPA.
|
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] |
474 |
Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues
|
[
"Youngjoon Jang",
"Haran Raajesh",
"Liliane Momeni",
"Gül Varol",
"Andrew Zisserman"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_Lost_in_Translation_Found_in_Context_Sign_Language_Translation_with_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_Lost_in_Translation_Found_in_Context_Sign_Language_Translation_with_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_Lost_in_Translation_CVPR_2025_supplemental.zip
|
2501.09754
|
@InProceedings{Jang_2025_CVPR,
author = {Jang, Youngjoon and Raajesh, Haran and Momeni, Liliane and Varol, G\"ul and Zisserman, Andrew},
title = {Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8742-8752}
}
|
Our objective is to translate continuous sign language into spoken language text. Inspired by the way human interpreters rely on context for accurate translation, we incorporate additional contextual cues together with the signing video, into a new translation framework. Specifically, besides visual sign recognition features that encode the input video, we integrate complementary textual information from (i) captions describing the background show, (ii) translation of previous sentences, as well as (iii) pseudo-glosses transcribing the signing. These are automatically extracted and inputted along with the visual features to a pre-trained large language model (LLM), which we fine-tune to generate spoken language translations in text form. Through extensive ablation studies, we show the positive contribution of each input cue to the translation performance. We train and evaluate our approach on BOBSL -- the largest British Sign Language dataset currently available. We show that our contextual approach significantly enhances the quality of the translations compared to previously reported results on BOBSL, and also to state-of-the-art methods that we implement as baselines. Furthermore, we demonstrate the generality of our approach by applying it also to How2Sign, an American Sign Language dataset, and achieve competitive results.
|
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] |
475 |
CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images
|
[
"Jungho Lee",
"Suhwan Cho",
"Taeoh Kim",
"Ho-Deok Jang",
"Minhyeok Lee",
"Geonho Cha",
"Dongyoon Wee",
"Dogyoon Lee",
"Sangyoun Lee"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_CoCoGaussian_Leveraging_Circle_of_Confusion_for_Gaussian_Splatting_from_Defocused_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_CoCoGaussian_Leveraging_Circle_of_Confusion_for_Gaussian_Splatting_from_Defocused_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_CoCoGaussian_Leveraging_Circle_CVPR_2025_supplemental.pdf
|
2412.16028
|
@InProceedings{Lee_2025_CVPR,
author = {Lee, Jungho and Cho, Suhwan and Kim, Taeoh and Jang, Ho-Deok and Lee, Minhyeok and Cha, Geonho and Wee, Dongyoon and Lee, Dogyoon and Lee, Sangyoun},
title = {CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {16101-16110}
}
|
3D Gaussian Splatting (3DGS) has attracted significant attention for its high-quality novel view rendering, inspiring research to address real-world challenges. While conventional methods depend on sharp images for accurate scene reconstruction, real-world scenarios are often affected by defocus blur due to finite depth of field, making it essential to account for realistic 3D scene representation. In this study, we propose CoCoGaussian, a Circle of Confusion-aware Gaussian Splatting that enables precise 3D scene representation using only defocused images. CoCoGaussian addresses the challenge of defocus blur by modeling the Circle of Confusion (CoC) through a physically grounded approach based on the principles of photographic defocus. Exploiting 3D Gaussians, we compute the CoC diameter from depth and learnable aperture information, generating multiple Gaussians to precisely capture the CoC shape. Furthermore, we introduce a learnable scaling factor to enhance robustness and provide more flexibility in handling unreliable depth in scenes with reflective or refractive surfaces. Experiments on both synthetic and real-world datasets demonstrate that CoCoGaussian achieves state-of-the-art performance across multiple benchmarks.
|
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] |
476 |
Semantic and Sequential Alignment for Referring Video Object Segmentation
|
[
"Feiyu Pan",
"Hao Fang",
"Fangkai Li",
"Yanyu Xu",
"Yawei Li",
"Luca Benini",
"Xiankai Lu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_Semantic_and_Sequential_Alignment_for_Referring_Video_Object_Segmentation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_Semantic_and_Sequential_Alignment_for_Referring_Video_Object_Segmentation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_Semantic_and_Sequential_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Pan_2025_CVPR,
author = {Pan, Feiyu and Fang, Hao and Li, Fangkai and Xu, Yanyu and Li, Yawei and Benini, Luca and Lu, Xiankai},
title = {Semantic and Sequential Alignment for Referring Video Object Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {19067-19076}
}
|
Referring video object segmentation (RVOS) seeks to segment the objects within a video referred by linguistic expressions. Existing RVOS solutions follow a "fuse then select" paradigm: establishing semantic correlation between visual and linguistic feature, and performing frame-level query interaction to select the instance mask per frame with instance segmentation module. This paradigm overlooks the challenge of semantic gap between the linguistic descriptor and the video object as well as the underlying clutters in the video. This paper proposes a novel Semantic and Sequential Alignment (SSA) paradigm to handle these challenges. We first insert a lightweight adapter after the vision language model (VLM) to perform the semantic alignment. Then, prior to selecting mask per frame, we exploit the trajectory-to-instance enhancement for each frame via sequential alignment. This paradigm leverages the visual-language alignment inherent in VLM during adaptation and tries to capture global information by ensembling trajectories. This helps understand videos and the corresponding descriptors by mitigating the discrepancy with intricate activity semantics, particularly when facing occlusion or similar interference. SSA demonstrates competitive performance while maintaining fewer learnable parameters.
|
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] |
477 |
Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition
|
[
"Juncheng Wang",
"Chao Xu",
"Cheng Yu",
"Lei Shang",
"Zhe Hu",
"Shujun Wang",
"Liefeng Bo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Synchronized_Video-to-Audio_Generation_via_Mel_Quantization-Continuum_Decomposition_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Synchronized_Video-to-Audio_Generation_via_Mel_Quantization-Continuum_Decomposition_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Synchronized_Video-to-Audio_Generation_CVPR_2025_supplemental.pdf
|
2503.06984
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Juncheng and Xu, Chao and Yu, Cheng and Shang, Lei and Hu, Zhe and Wang, Shujun and Bo, Liefeng},
title = {Synchronized Video-to-Audio Generation via Mel Quantization-Continuum Decomposition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {3111-3120}
}
|
Video-to-audio generation is essential for synthesizing realistic audio tracks that synchronize effectively with silent videos.Following the perspective of extracting essential signals from videos that can precisely control the mature text-to-audio generative diffusion models, this paper presents how to balance the representation of mel-spectrograms in terms of completeness and complexity through a new approach called Mel Quantization-Continuum Decomposition (Mel-QCD).We decompose the mel-spectrogram into three distinct types of signals, employing quantization or continuity to them, we can effectively predict them from video by a devised video-to-all (V2X) predictor.Then, the predicted signals are recomposed and fed into a ControlNet, along with a textual inversion design, to control the audio generation process.Our proposed Mel-QCD method demonstrates state-of-the-art performance across eight metrics, evaluating dimensions such as quality, synchronization, and semantic consistency.
|
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] |
478 |
Continual SFT Matches Multimodal RLHF with Negative Supervision
|
[
"Ke Zhu",
"Yu Wang",
"Yanpeng Sun",
"Qiang Chen",
"Jiangjiang Liu",
"Gang Zhang",
"Jingdong Wang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Continual_SFT_Matches_Multimodal_RLHF_with_Negative_Supervision_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Continual_SFT_Matches_Multimodal_RLHF_with_Negative_Supervision_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Continual_SFT_Matches_CVPR_2025_supplemental.pdf
|
2411.14797
|
@InProceedings{Zhu_2025_CVPR,
author = {Zhu, Ke and Wang, Yu and Sun, Yanpeng and Chen, Qiang and Liu, Jiangjiang and Zhang, Gang and Wang, Jingdong},
title = {Continual SFT Matches Multimodal RLHF with Negative Supervision},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14615-14624}
}
|
Multimodal RLHF usually happens after supervised finetuning (SFT) stage to continually improve vision-language models' (VLMs) comprehension. Conventional wisdom holds its superiority over continual SFT during this preference alignment stage. In this paper, we observe that the inherent value of multimodal RLHF lies in its negative supervision, the logit of the rejected responses. We thus propose a novel negative supervised finetuning (nSFT) approach that fully excavates these information resided. Our nSFT disentangles this negative supervision in RLHF paradigm, and continually aligns VLMs with a simple SFT loss. This is more memory efficient than multimodal RLHF where 2 (e.g., DPO) or 4 (e.g., PPO) large VLMs are strictly required. The effectiveness of nSFT is rigorously proved by comparing it with various multimodal RLHF approaches, across different dataset sources, base VLMs and evaluation metrics. Besides, fruitful of ablations are provided to support our hypothesis. Code will be found in https://github.com/Kevinz-code/nSFT/.
|
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] |
479 |
Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition
|
[
"Anqi Zhu",
"Jingmin Zhu",
"James Bailey",
"Mingming Gong",
"Qiuhong Ke"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Semantic-guided_Cross-Modal_Prompt_Learning_for_Skeleton-based_Zero-shot_Action_Recognition_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Semantic-guided_Cross-Modal_Prompt_Learning_for_Skeleton-based_Zero-shot_Action_Recognition_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Semantic-guided_Cross-Modal_Prompt_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Zhu_2025_CVPR,
author = {Zhu, Anqi and Zhu, Jingmin and Bailey, James and Gong, Mingming and Ke, Qiuhong},
title = {Semantic-guided Cross-Modal Prompt Learning for Skeleton-based Zero-shot Action Recognition},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13876-13885}
}
|
Skeleton-based human action recognition is promising due to its privacy preservation, robustness to visual challenges, and computational efficiency. Especially, the practical necessity to recognize unseen actions has led to increased interest in zero-shot skeleton-based action recognition (ZSSAR). Existing ZSSAR approaches often rely on manually crafted action descriptions or visual assumptions to enhance knowledge transfer, which is limited in flexibility and prone to inaccuracies and noise. To overcome this, we introduce Semantic-guided Cross-Modal Prompt Learning (SCoPLe), a novel framework that replaces manual guidance with data-driven prompt learning for refinement and alignment of skeletal and textual features. Specifically, we introduce a dual-stream language prompting module that preserves the original semantic context from the pre-trained text encoder while still effectively tuning its ouput for ZSSAR task adaptation. We also introduce a joint-shaped prompting module that learns tuning for skeleton features and incorporate an adaptive visual representation sampler that leverages text semantics to strengthen the cross-modal prompting interactions during skeleton-to-text embedding projection. Experimental results on the NTU-RGB+D and PKU-MMD datasets demonstrate the state-of-the-art performance of our method in both ZSSAR and generalized ZSSAR scenarios.
|
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] |
480 |
FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video
|
[
"Jiawei Zhang",
"Zijian Wu",
"Zhiyang Liang",
"Yicheng Gong",
"Dongfang Hu",
"Yao Yao",
"Xun Cao",
"Hao Zhu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_FATE_Full-head_Gaussian_Avatar_with_Textural_Editing_from_Monocular_Video_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_FATE_Full-head_Gaussian_Avatar_with_Textural_Editing_from_Monocular_Video_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_FATE_Full-head_Gaussian_CVPR_2025_supplemental.zip
|
2411.15604
|
@InProceedings{Zhang_2025_CVPR,
author = {Zhang, Jiawei and Wu, Zijian and Liang, Zhiyang and Gong, Yicheng and Hu, Dongfang and Yao, Yao and Cao, Xun and Zhu, Hao},
title = {FATE: Full-head Gaussian Avatar with Textural Editing from Monocular Video},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5535-5545}
}
|
Reconstructing high-fidelity, animatable 3D head avatars from effortlessly captured monocular videos is a pivotal yet formidable challenge. Although significant progress has been made in rendering performance and manipulation capabilities, notable challenges remain, including incomplete reconstruction and inefficient Gaussian representation. To address these challenges, we introduce FATE -- a novel method for reconstructing an editable full-head avatar from a single monocular video. FATE integrates a sampling-based densification strategy to ensure optimal positional distribution of points, improving rendering efficiency. A neural baking technique is introduced to convert discrete Gaussian representations into continuous attribute maps, facilitating intuitive appearance editing. Furthermore, we propose a universal completion framework to recover non-frontal appearance, culminating in a 360^\circ-renderable 3D head avatar. FATE outperforms previous approaches in both qualitative and quantitative evaluations, achieving state-of-the-art performance. To the best of our knowledge, FATE is the first animatable and 360^\circ full-head monocular reconstruction method for a 3D head avatar. Project page and code are available at this \href https://zjwfufu.github.io/FATE-page/ link .
|
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] |
481 |
ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
|
[
"Chengyou Jia",
"Changliang Xia",
"Zhuohang Dang",
"Weijia Wu",
"Hangwei Qian",
"Minnan Luo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Jia_ChatGen_Automatic_Text-to-Image_Generation_From_FreeStyle_Chatting_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_ChatGen_Automatic_Text-to-Image_Generation_From_FreeStyle_Chatting_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jia_ChatGen_Automatic_Text-to-Image_CVPR_2025_supplemental.pdf
|
2411.17176
|
@InProceedings{Jia_2025_CVPR,
author = {Jia, Chengyou and Xia, Changliang and Dang, Zhuohang and Wu, Weijia and Qian, Hangwei and Luo, Minnan},
title = {ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13284-13293}
}
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Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios. This challenge arises from the complexity and uncertainty of tedious steps such as crafting suitable prompts, selecting appropriate models, and configuring specific arguments, making users resort to labor-intensive attempts for desired images. This paper proposes Automatic T2I generation, which aims to automate these tedious steps, allowing users to simply describe their needs in a freestyle chatting way. To systematically study this problem, we first introduce ChatGenBench, a novel benchmark designed for Automatic T2I. It features high-quality paired data with diverse freestyle inputs, enabling comprehensive evaluation of automatic T2I models across all steps. Additionally, recognizing Automatic T2I as a complex multi-step reasoning task, we propose ChatGen-Evo, a multi-stage evolution strategy that progressively equips models with essential automation skills. Through extensive evaluation across step-wise accuracy and image quality, ChatGen-Evo significantly enhances performance over various baselines. Our evaluation also uncovers valuable insights for advancing automatic T2I. All our data, code and models will be publicly available.
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] |
482 |
GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control
|
[
"Mariam Hassan",
"Sebastian Stapf",
"Ahmad Rahimi",
"Pedro M B Rezende",
"Yasaman Haghighi",
"David Brüggemann",
"Isinsu Katircioglu",
"Lin Zhang",
"Xiaoran Chen",
"Suman Saha",
"Marco Cannici",
"Elie Aljalbout",
"Botao Ye",
"Xi Wang",
"Aram Davtyan",
"Mathieu Salzmann",
"Davide Scaramuzza",
"Marc Pollefeys",
"Paolo Favaro",
"Alexandre Alahi"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Hassan_GEM_A_Generalizable_Ego-Vision_Multimodal_World_Model_for_Fine-Grained_Ego-Motion_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Hassan_GEM_A_Generalizable_Ego-Vision_Multimodal_World_Model_for_Fine-Grained_Ego-Motion_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hassan_GEM_A_Generalizable_CVPR_2025_supplemental.pdf
|
2412.11198
|
@InProceedings{Hassan_2025_CVPR,
author = {Hassan, Mariam and Stapf, Sebastian and Rahimi, Ahmad and Rezende, Pedro M B and Haghighi, Yasaman and Br\"uggemann, David and Katircioglu, Isinsu and Zhang, Lin and Chen, Xiaoran and Saha, Suman and Cannici, Marco and Aljalbout, Elie and Ye, Botao and Wang, Xi and Davtyan, Aram and Salzmann, Mathieu and Scaramuzza, Davide and Pollefeys, Marc and Favaro, Paolo and Alahi, Alexandre},
title = {GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {22404-22415}
}
|
We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, our model has precise control over object dynamics, ego-agent motion and human poses. GEM generates paired RGB and depth outputs for richer spatial understanding. We introduce autoregressive noise schedules to enable stable long-horizon generations. Our dataset is comprised of 4000+ hours of multimodal data across domains like autonomous driving, egocentric human activities, and drone flights. Pseudo-labels are used to get depth maps, ego-trajectories, and human poses. We use a comprehensive evaluation framework, including a new Control of Object Manipulation (COM) metric, to assess controllability. Experiments show GEM excels at generating diverse, controllable scenarios and temporal consistency over long generations. Code, models, and datasets are fully open-sourced.
|
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483 |
VEU-Bench: Towards Comprehensive Understanding of Video Editing
|
[
"Bozheng Li",
"Yongliang Wu",
"Yi Lu",
"Jiashuo Yu",
"Licheng Tang",
"Jiawang Cao",
"Wenqing Zhu",
"Yuyang Sun",
"Jay Wu",
"Wenbo Zhu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Li_VEU-Bench_Towards_Comprehensive_Understanding_of_Video_Editing_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_VEU-Bench_Towards_Comprehensive_Understanding_of_Video_Editing_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_VEU-Bench_Towards_Comprehensive_CVPR_2025_supplemental.zip
| null |
@InProceedings{Li_2025_CVPR,
author = {Li, Bozheng and Wu, Yongliang and Lu, Yi and Yu, Jiashuo and Tang, Licheng and Cao, Jiawang and Zhu, Wenqing and Sun, Yuyang and Wu, Jay and Zhu, Wenbo},
title = {VEU-Bench: Towards Comprehensive Understanding of Video Editing},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13671-13680}
}
|
Widely shared videos on the internet are often edited. Recently, although Video Large Language Models (Vid-LLMs) have made great progress in general video understanding tasks, their capabilities in video editing understanding (VEU) tasks remain unexplored. To address this gap, in this paper, we introduce VEU-Bench (Video Editing Understanding Benchmark), a comprehensive benchmark that categorizes video editing components across various dimensions, from intra-frame features like shot size to inter-shot attributes such as cut types and transitions. Unlike previous video editing understanding benchmarks that focus mainly on editing element classification, VEU-Bench encompasses 19 fine-grained tasks across three stages: recognition, reasoning, and judging. To enhance the annotation of VEU automatically, we built an annotation pipeline integrated with an ontology-based knowledge base. Through extensive experiments with 11 state-of-the-art Vid-LLMs, our findings reveal that current Vid-LLMs face significant challenges in VEU tasks, with some performing worse than random choice. To alleviate this issue, we develop Oscars(Named after the Academy Awards.), a VEU expert model fine-tuned on the curated VEU-Bench dataset. It outperforms existing open-source Vid-LLMs on VEU-Bench by over 28.3% in accuracy and achieves performance comparable to commercial models like GPT-4o. We also demonstrate that incorporating VEU data significantly enhances the performance of Vid-LLMs on general video understanding benchmarks, with an average improvement of 8.3% across nine reasoning tasks. The code and data will be made available.
|
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] |
484 |
Decouple Distortion from Perception: Region Adaptive Diffusion for Extreme-low Bitrate Perception Image Compression
|
[
"Jinchang Xu",
"Shaokang Wang",
"Jintao Chen",
"Zhe Li",
"Peidong Jia",
"Fei Zhao",
"Guoqing Xiang",
"Zhijian Hao",
"Shanghang Zhang",
"Xiaodong Xie"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Decouple_Distortion_from_Perception_Region_Adaptive_Diffusion_for_Extreme-low_Bitrate_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Decouple_Distortion_from_Perception_Region_Adaptive_Diffusion_for_Extreme-low_Bitrate_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Decouple_Distortion_from_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Xu_2025_CVPR,
author = {Xu, Jinchang and Wang, Shaokang and Chen, Jintao and Li, Zhe and Jia, Peidong and Zhao, Fei and Xiang, Guoqing and Hao, Zhijian and Zhang, Shanghang and Xie, Xiaodong},
title = {Decouple Distortion from Perception: Region Adaptive Diffusion for Extreme-low Bitrate Perception Image Compression},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18051-18061}
}
|
Leveraging the generative power of diffusion models, generative image compression has achieved impressive perceptual fidelity even at extremely low bitrates. However, current methods often neglect the non-uniform complexity of images, limiting their ability to balance global perceptual quality with local texture consistency and to allocate coding resources efficiently. To address this, we introduce the Map-guided Masking Realism Image Diffusion Codec(MRIDC), designed to optimize the trade-off between local distortion and global perceptual quality in extreme-low bitrate compression. MRIDC integrates a vector-quantized image encoder with a diffusion-based decoder. On the encoding side, we propose a Map-guided Latent Masking(MLM) module, which selectively masks elements in the latent space based on prior information, allowing adaptive resource allocation aligned with image complexity. On the decoding side, masked latents are completed using the Bidirectional Prediction Controllable Generation(BPCG) module, which guides the constrained generation process within the diffusion model to reconstruct the image. Experimental results show that MRIDC achieves state-of-the-art perceptual compression quality at extremely low bitrates, effectively preserving feature consistency in key regions and advancing the rate-distortion-perception performance curve, establishing new benchmarks in balancing compression efficiency with visual fidelity.
|
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485 |
Yo'Chameleon: Personalized Vision and Language Generation
|
[
"Thao Nguyen",
"Krishna Kumar Singh",
"Jing Shi",
"Trung Bui",
"Yong Jae Lee",
"Yuheng Li"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_YoChameleon_Personalized_Vision_and_Language_Generation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_YoChameleon_Personalized_Vision_and_Language_Generation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_YoChameleon_Personalized_Vision_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Nguyen_2025_CVPR,
author = {Nguyen, Thao and Singh, Krishna Kumar and Shi, Jing and Bui, Trung and Lee, Yong Jae and Li, Yuheng},
title = {Yo'Chameleon: Personalized Vision and Language Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {14438-14448}
}
|
Large Multimodal Models (e.g., GPT-4, Gemini, Chameleon) have evolved into powerful tools with millions of users. However, they remain generic models and lack personalized knowledge of specific user concepts. Previous work has explored personalization for text generation, yet it remains unclear how these methods can be adapted to new modalities, such as image generation. In this paper, we introduce Yo'Chameleon, the first attempt to study personalization for large multimodal models. Given 3-5 images of a particular concept, Yo'Chameleon leverages soft-prompt tuning to embed subject-specific information to (i) answer questions about the subject and (ii) recreate pixel-level details to produce images of the subject in new contexts. Yo'Chameleon is trained with (i) a self-prompting optimization mechanism to balance performance across multiple modalities, and (ii) a "soft-positive" image generation approach to enhance image quality in a few-shot setting. Our qualitative and quantitative analyses reveal that Yo'Chameleon can learn concepts more efficiently using fewer tokens and effectively encode visual attributes, outperforming prompting baselines.
|
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] |
486 |
PatchVSR: Breaking Video Diffusion Resolution Limits with Patch-wise Video Super-Resolution
|
[
"Shian Du",
"Menghan Xia",
"Chang Liu",
"Xintao Wang",
"Jing Wang",
"Pengfei Wan",
"Di Zhang",
"Xiangyang Ji"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Du_PatchVSR_Breaking_Video_Diffusion_Resolution_Limits_with_Patch-wise_Video_Super-Resolution_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_PatchVSR_Breaking_Video_Diffusion_Resolution_Limits_with_Patch-wise_Video_Super-Resolution_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_PatchVSR_Breaking_Video_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Du_2025_CVPR,
author = {Du, Shian and Xia, Menghan and Liu, Chang and Wang, Xintao and Wang, Jing and Wan, Pengfei and Zhang, Di and Ji, Xiangyang},
title = {PatchVSR: Breaking Video Diffusion Resolution Limits with Patch-wise Video Super-Resolution},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17799-17809}
}
|
Pre-trained video generation models hold great potential for generative video super-resolution (VSR). However, adapting them for full-size VSR, as most existing methods do, suffers from unnecessary intensive full-attention computation and fixed output resolution. To overcome these limitations, we make the first exploration into utilizing video diffusion priors for patch-wise VSR.This is non-trivial because pre-trained video diffusion models are not native for patch-level detail generation. To mitigate this challenge, we propose an innovative approach, called PatchVSR, which integrates a dual-stream adapter for conditional guidance. The patch branch extracts features from input patches to maintain content fidelity while the global branch extracts context features from the resized full video to bridge the generation gap caused byincomplete semantics of patches.Particularly, we also inject the patch's location information into the model to better contextualize patch synthesis within the global video frame.Experiments demonstrate that our method can synthesize high-fidelity, high-resolution details at the patch level. A tailor-made multi-patch joint modulation is proposed to ensure visual consistency across individually enhanced patches. Due to the flexibility of our patch-based paradigm, we can achieve highly competitive 4K VSR based on a 512x512 resolution base model, with extremely high efficiency.
|
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] |
487 |
FluxSpace: Disentangled Semantic Editing in Rectified Flow Models
|
[
"Yusuf Dalva",
"Kavana Venkatesh",
"Pinar Yanardag"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Dalva_FluxSpace_Disentangled_Semantic_Editing_in_Rectified_Flow_Models_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Dalva_FluxSpace_Disentangled_Semantic_Editing_in_Rectified_Flow_Models_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dalva_FluxSpace_Disentangled_Semantic_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Dalva_2025_CVPR,
author = {Dalva, Yusuf and Venkatesh, Kavana and Yanardag, Pinar},
title = {FluxSpace: Disentangled Semantic Editing in Rectified Flow Models},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13083-13092}
}
|
Rectified flow models have emerged as a dominant approach in image generation, showcasing impressive capabilities in high-quality image synthesis. However, despite their effectiveness in visual generation, rectified flow models often struggle with disentangled editing of images. This limitation prevents the ability to perform precise, attribute-specific modifications without affecting unrelated aspects of the image. In this paper, we introduce FluxSpace, a domain-agnostic image editing method leveraging a representation space with the ability to control the semantics of images generated by rectified flow transformers, such as Flux. By leveraging the representations learned by the transformer blocks within the rectified flow models, we propose a set of semantically interpretable representations that enable a wide range of image editing tasks, from fine-grained image editing to artistic creation. This work offers a scalable and effective image editing approach, along with its disentanglement capabilities.
|
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] |
488 |
Scene-Centric Unsupervised Panoptic Segmentation
|
[
"Oliver Hahn",
"Christoph Reich",
"Nikita Araslanov",
"Daniel Cremers",
"Christian Rupprecht",
"Stefan Roth"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Hahn_Scene-Centric_Unsupervised_Panoptic_Segmentation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Hahn_Scene-Centric_Unsupervised_Panoptic_Segmentation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hahn_Scene-Centric_Unsupervised_Panoptic_CVPR_2025_supplemental.pdf
|
2504.01955
|
@InProceedings{Hahn_2025_CVPR,
author = {Hahn, Oliver and Reich, Christoph and Araslanov, Nikita and Cremers, Daniel and Rupprecht, Christian and Roth, Stefan},
title = {Scene-Centric Unsupervised Panoptic Segmentation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24485-24495}
}
|
Unsupervised panoptic segmentation aims to partition an image into semantically meaningful regions and distinct object instances without training on manually annotated data. In contrast to prior work on unsupervised panoptic scene understanding, we eliminate the need for object-centric training data, enabling the unsupervised understanding of complex scenes. To that end, we present the first unsupervised panoptic method that directly trains on scene-centric imagery. In particular, we propose an approach to obtain high-resolution panoptic pseudo labels on complex scene-centric data, combining visual representations, depth, and motion cues. Utilizing both pseudo-label training and a panoptic self-training strategy yields a novel approach that accurately predicts panoptic segmentation of complex scenes without requiring any human annotations. Our approach significantly improves panoptic quality, e.g., surpassing the recent state of the art in unsupervised panoptic segmentation on Cityscapes by 9.4% points in PQ.
|
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] |
489 |
Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction
|
[
"Yuanbo Wang",
"Zhaoxuan Zhang",
"Jiajin Qiu",
"Dilong Sun",
"Zhengyu Meng",
"Xiaopeng Wei",
"Xin Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Touch2Shape_Touch-Conditioned_3D_Diffusion_for_Shape_Exploration_and_Reconstruction_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Touch2Shape_Touch-Conditioned_3D_Diffusion_for_Shape_Exploration_and_Reconstruction_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Touch2Shape_Touch-Conditioned_3D_CVPR_2025_supplemental.pdf
|
2505.13091
|
@InProceedings{Wang_2025_CVPR,
author = {Wang, Yuanbo and Zhang, Zhaoxuan and Qiu, Jiajin and Sun, Dilong and Meng, Zhengyu and Wei, Xiaopeng and Yang, Xin},
title = {Touch2Shape: Touch-Conditioned 3D Diffusion for Shape Exploration and Reconstruction},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {5656-5665}
}
|
Diffusion models have made breakthroughs in 3D generation tasks. Current 3D diffusion models focus on reconstructing target shape from images or a set of partial observations. While excelling in global context understanding, they struggle to capture the local details of complex shapes and limited to the occlusion and lighting conditions. To overcome these limitations, we utilize tactile images to capture the local 3D information and propose a Touch2Shape model, which leverages a touch-conditioned diffusion model to explore and reconstruct the target shape from touch. For shape reconstruction, we have developed a touch embedding module to condition the diffusion model in creating a compact representation and a touch shape fusion module to refine the reconstructed shape. For shape exploration, we combine the diffusion model with reinforcement learning to train a policy. This involves using the generated latent vector from the diffusion model to guide the touch exploration policy training through a novel reward design. Experiments validate the reconstruction quality thorough both qualitatively and quantitative analysis, and our touch exploration policy further boosts reconstruction performance.
|
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] |
490 |
VITED: Video Temporal Evidence Distillation
|
[
"Yujie Lu",
"Yale Song",
"William Wang",
"Lorenzo Torresani",
"Tushar Nagarajan"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_VITED_Video_Temporal_Evidence_Distillation_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_VITED_Video_Temporal_Evidence_Distillation_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_VITED_Video_Temporal_CVPR_2025_supplemental.pdf
|
2503.12855
|
@InProceedings{Lu_2025_CVPR,
author = {Lu, Yujie and Song, Yale and Wang, William and Torresani, Lorenzo and Nagarajan, Tushar},
title = {VITED: Video Temporal Evidence Distillation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {8501-8511}
}
|
We investigate complex video question answering via chain-of-evidence reasoning --- identifying sequences of temporal spans from multiple relevant parts of the video, together with visual evidence within them.Existing models struggle with multi-step reasoning as they uniformly sample a fixed number of frames, which can miss critical evidence distributed nonuniformly throughout the video. Moreover, they lack the ability to temporally localize such evidence in the broader context of the full video, which is required for answering complex questions. We propose a framework to enhance existing VideoQA datasets with evidence reasoning chains, automatically constructed by searching for optimal intervals of interest in the video with supporting evidence, that maximizes the likelihood of answering a given question.We train our model (ViTED) to generate these evidence chains directly, enabling it to both localize evidence windows as well as perform multi-step reasoning across them in long-form video content.We show the value of our evidence-distilled models on a suite of long video QA benchmarks where we outperform state-of-the-art approaches that lack evidence reasoning capabilities.
|
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] |
491 |
Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization
|
[
"Zhipeng Xu",
"De Cheng",
"Xinyang Jiang",
"Nannan Wang",
"Dongsheng Li",
"Xinbo Gao"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Adversarial_Domain_Prompt_Tuning_and_Generation_for_Single_Domain_Generalization_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Adversarial_Domain_Prompt_Tuning_and_Generation_for_Single_Domain_Generalization_CVPR_2025_paper.pdf
| null | null |
@InProceedings{Xu_2025_CVPR,
author = {Xu, Zhipeng and Cheng, De and Jiang, Xinyang and Wang, Nannan and Li, Dongsheng and Gao, Xinbo},
title = {Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {18584-18595}
}
|
Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain available for training. One of the promising directions for achieving single-domain generalization is to generate out-of-domain (OOD) training data through data augmentation or image generation. Given the rapid advancements in AI-generated content (AIGC), this paper is the first to propose leveraging powerful pre-trained text-to-image (T2I) foundation models to create the training data. However, manually designing textual prompts to generate images for all possible domains is often impractical, and some domain characteristics may be too abstract to describe with words. To address these challenges, we propose a novel Progressive Adversarial Prompt Tuning (PAPT) framework for pre-trained diffusion models. Instead of relying on static textual domains, our approach learns two sets of abstract prompts as conditions for the diffusion model: one that captures domain-invariant category information and another that models domain-specific styles. This adversarial learning mechanism enables the T2I model to generate images in various domain styles while preserving key categorical features. Extensive experiments demonstrate the effectiveness of the proposed method, achieving superior performances to state-of-the-art single-domain generalization approaches.
|
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] |
492 |
Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems
|
[
"Alejandro Castañeda Garcia",
"Jan Warchocki",
"Jan van Gemert",
"Daan Brinks",
"Nergis Tomen"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Garcia_Learning_Physics_From_Video_Unsupervised_Physical_Parameter_Estimation_for_Continuous_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Garcia_Learning_Physics_From_Video_Unsupervised_Physical_Parameter_Estimation_for_Continuous_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Garcia_Learning_Physics_From_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Garcia_2025_CVPR,
author = {Garcia, Alejandro Casta\~neda and Warchocki, Jan and van Gemert, Jan and Brinks, Daan and Tomen, Nergis},
title = {Learning Physics From Video: Unsupervised Physical Parameter Estimation for Continuous Dynamical Systems},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {27924-27933}
}
|
Extracting physical dynamical system parameters from recorded observations is key in natural science. Current methods for automatic parameter estimation from video train supervised deep networks on large datasets. Such datasets require labels, which are difficult to acquire. While some unsupervised techniques--which depend on frame prediction--exist, they suffer from long training times, initialization instabilities, only consider motion-based dynamical systems, and are evaluated mainly on synthetic data. In this work, we propose an unsupervised method to estimate the physical parameters of known, continuous governing equations from single videos suitable for different dynamical systems beyond motion and robust to initialization. Moreover, we remove the need for frame prediction by implementing a KL-divergence-based loss function in the latent space, which avoids convergence to trivial solutions and reduces model size and compute. We first evaluate our model on synthetic data, as commonly done. After which, we take the field closer to reality by recording Delfys75: our own real-world dataset of 75 videos for five different types of dynamical systems to evaluate our method and others. Our method compares favorably to others. Code and data are available online: https://github.com/Alejandro-neuro/Learning_physics_from_video.
|
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] |
493 |
Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts
|
[
"Yu Cao",
"Zengqun Zhao",
"Ioannis Patras",
"Shaogang Gong"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_Temporal_Score_Analysis_for_Understanding_and_Correcting_Diffusion_Artifacts_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_Temporal_Score_Analysis_for_Understanding_and_Correcting_Diffusion_Artifacts_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_Temporal_Score_Analysis_CVPR_2025_supplemental.pdf
|
2503.16218
|
@InProceedings{Cao_2025_CVPR,
author = {Cao, Yu and Zhao, Zengqun and Patras, Ioannis and Gong, Shaogang},
title = {Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {7707-7716}
}
|
Visual artifacts remain a persistent challenge in diffusion models, even with training on massive datasets. Current solutions primarily rely on supervised detectors, yet lack understanding of why these artifacts occur in the first place. In our analysis, we identify three distinct phases in the diffusion generative process: Profiling, Mutation, and Refinement. Artifacts typically emerge during the Mutation phase, where certain regions exhibit anomalous score dynamics over time, causing abrupt disruptions in the normal evolution pattern. This temporal nature explains why existing methods focusing only on spatial uncertainty of the final output fail at effective artifact localization. Based on these insights, we propose ASCED (Abnormal Score Correction for Enhancing Diffusion), that detects artifacts by monitoring abnormal score dynamics during the diffusion process, with a trajectory-aware on-the-fly mitigation strategy that appropriate generation of noise in the detected areas. Unlike most existing methods that apply post hoc corrections, e.g., by applying a noising-denoising scheme after generation, our mitigation strategy operates seamlessly within the existing diffusion process. Extensive experiments demonstrate that our proposed approach effectively reduces artifacts across diverse domains, matching or surpassing existing supervised methods without additional training.
|
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] |
494 |
ProAPO: Progressively Automatic Prompt Optimization for Visual Classification
|
[
"Xiangyan Qu",
"Gaopeng Gou",
"Jiamin Zhuang",
"Jing Yu",
"Kun Song",
"Qihao Wang",
"Yili Li",
"Gang Xiong"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_ProAPO_Progressively_Automatic_Prompt_Optimization_for_Visual_Classification_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_ProAPO_Progressively_Automatic_Prompt_Optimization_for_Visual_Classification_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_ProAPO_Progressively_Automatic_CVPR_2025_supplemental.pdf
|
2502.19844
|
@InProceedings{Qu_2025_CVPR,
author = {Qu, Xiangyan and Gou, Gaopeng and Zhuang, Jiamin and Yu, Jing and Song, Kun and Wang, Qihao and Li, Yili and Xiong, Gang},
title = {ProAPO: Progressively Automatic Prompt Optimization for Visual Classification},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {25145-25155}
}
|
Vision-language models (VLMs) have made significant progress in image classification by training with large-scale paired image-text data. Their performances largely depend on the prompt quality. While recent methods show that visual descriptions generated by large language models (LLMs) enhance the generalization of VLMs, class-specific prompts may be inaccurate or lack discrimination due to the hallucination in LLMs. In this paper, we aim to find visually discriminative prompts for fine-grained categories with minimal supervision and no human-in-the-loop. An evolution-based algorithm is proposed to progressively optimize language prompts from task-specific templates to class-specific descriptions. Unlike optimizing templates, the search space shows an explosion in class-specific candidate prompts. This increases prompt generation costs, iterative times, and the overfitting problem. To this end, we first introduce several simple yet effective edit-based and evolution-based operations to generate diverse candidate prompts by one-time query of LLMs. Then, two sampling strategies are proposed to find a better initial search point and reduce traversed categories, saving iteration costs. Moreover, we apply a novel fitness score with entropy constraints to mitigate overfitting. In a challenging one-shot image classification setting, our method outperforms existing textual prompt-based methods and improves LLM-generated description methods across 13 datasets. Meanwhile, we demonstrate that our optimal prompts improve adapter-based methods and transfer effectively across different backbones.
|
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] |
495 |
ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts
|
[
"Dmitry Petrov",
"Pradyumn Goyal",
"Divyansh Shivashok",
"Yuanming Tao",
"Melinos Averkiou",
"Evangelos Kalogerakis"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Petrov_ShapeWords_Guiding_Text-to-Image_Synthesis_with_3D_Shape-Aware_Prompts_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Petrov_ShapeWords_Guiding_Text-to-Image_Synthesis_with_3D_Shape-Aware_Prompts_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Petrov_ShapeWords_Guiding_Text-to-Image_CVPR_2025_supplemental.pdf
|
2412.02912
|
@InProceedings{Petrov_2025_CVPR,
author = {Petrov, Dmitry and Goyal, Pradyumn and Shivashok, Divyansh and Tao, Yuanming and Averkiou, Melinos and Kalogerakis, Evangelos},
title = {ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13305-13314}
}
|
We introduce ShapeWords, an approach for synthesizing images based on 3D shape guidance and text prompts.ShapeWords incorporates target 3D shape information within specialized tokens embedded together with the input text, effectively blending 3D shape awareness with textual context to guide the image synthesis process. Unlike conventional shape guidance methods that rely on depth maps restricted to fixed viewpoints and often overlook full 3D structure or textual context, ShapeWords generates diverse yet consistent images that reflect both the target shape's geometry and the textual description. Experimental results show that ShapeWords produces images that are more text-compliant, aesthetically plausible, while also maintaining 3D shape awareness.
|
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] |
496 |
Auto-Encoded Supervision for Perceptual Image Super-Resolution
|
[
"MinKyu Lee",
"Sangeek Hyun",
"Woojin Jun",
"Jae-Pil Heo"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Auto-Encoded_Supervision_for_Perceptual_Image_Super-Resolution_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Auto-Encoded_Supervision_for_Perceptual_Image_Super-Resolution_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Auto-Encoded_Supervision_for_CVPR_2025_supplemental.pdf
|
2412.00124
|
@InProceedings{Lee_2025_CVPR,
author = {Lee, MinKyu and Hyun, Sangeek and Jun, Woojin and Heo, Jae-Pil},
title = {Auto-Encoded Supervision for Perceptual Image Super-Resolution},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {17958-17968}
}
|
This work tackles the fidelity objective in the perceptual super-resolution (SR) task. Specifically, we address the shortcomings of pixel-level \mathcal L _\text p loss (\mathcal L _\text pix ) in the GAN-based SR framework. Since \mathcal L _\text pix is known to have a trade-off relationship against perceptual quality, prior methods often multiply a small scale factor or utilize low-pass filters. However, this work shows that these circumventions fail to address the fundamental factor that induces blurring. Accordingly, we focus on two points: 1) precisely discriminating the subcomponent of \mathcal L _\text pix that contributes to blurring, and 2) guiding reconstruction only based on the factor that is free from this trade-off relationship. We show that this can be achieved in a surprisingly simple manner, with an Auto-Encoder (AE) pretrained using \mathcal L _\text pix . Based on this insight, we propose the Auto-Encoded Supervision for Optimal Penalization loss (\mathcal L _\text AESOP ), a novel loss function that measures distance in the AE space (the space after the decoder, not the bottleneck), rather than in the raw pixel space. By simply substituting \mathcal L _\text pix with \mathcal L _\text AESOP , we can provide effective reconstruction guidance without compromising perceptual quality. Designed for simplicity, our method enables easy integration into existing SR frameworks. Extensive experiments demonstrate the effectiveness of AESOP.
|
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] |
497 |
Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events
|
[
"Aditya Chinchure",
"Sahithya Ravi",
"Raymond Ng",
"Vered Shwartz",
"Boyang Li",
"Leonid Sigal"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Chinchure_Black_Swan_Abductive_and_Defeasible_Video_Reasoning_in_Unpredictable_Events_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Chinchure_Black_Swan_Abductive_and_Defeasible_Video_Reasoning_in_Unpredictable_Events_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chinchure_Black_Swan_Abductive_CVPR_2025_supplemental.zip
|
2412.05725
|
@InProceedings{Chinchure_2025_CVPR,
author = {Chinchure, Aditya and Ravi, Sahithya and Ng, Raymond and Shwartz, Vered and Li, Boyang and Sigal, Leonid},
title = {Black Swan: Abductive and Defeasible Video Reasoning in Unpredictable Events},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {24201-24210}
}
|
The commonsense reasoning capabilities of vision-language models (VLMs), especially in abductive reasoning and defeasible reasoning, remain poorly understood. Most benchmarks focus on typical visual scenarios, making it difficult to discern whether model performance stems from keen perception and reasoning skills, or reliance on pure statistical recall. We argue that by focusing on atypical events in videos, clearer insights can be gained on the core capabilities of VLMs. Explaining and understanding such out-of-distribution events requires models to extend beyond basic pattern recognition and regurgitation of their prior knowledge. To this end, we introduce BlackSwanSuite, a benchmark for evaluating VLMs' ability to reason about unexpected events through abductive and defeasible tasks. Our tasks artificially limit the amount of visual information provided to models while questioning them about hidden unexpected events, or provide new visual information that could change an existing hypothesis about the event. We curate a comprehensive benchmark suite comprising over 3,800 MCQ, 4,900 generative and 6,700 yes/no questions, spanning 1,655 videos. After extensively evaluating various state-of-the-art VLMs, including GPT-4o and Gemini 1.5 Pro, as well as open-source VLMs such as LLaVA-Video, we find significant performance gaps of up to 32% from humans on these tasks. Our findings reveal key limitations in current VLMs, emphasizing the need for enhanced model architectures and training strategies. Our data and leaderboard is available at blackswan.cs.ubc.ca.
|
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-0.019957423210144043
] |
498 |
Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise
|
[
"Ryan Burgert",
"Yuancheng Xu",
"Wenqi Xian",
"Oliver Pilarski",
"Pascal Clausen",
"Mingming He",
"Li Ma",
"Yitong Deng",
"Lingxiao Li",
"Mohsen Mousavi",
"Michael Ryoo",
"Paul Debevec",
"Ning Yu"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Burgert_Go-with-the-Flow_Motion-Controllable_Video_Diffusion_Models_Using_Real-Time_Warped_Noise_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Burgert_Go-with-the-Flow_Motion-Controllable_Video_Diffusion_Models_Using_Real-Time_Warped_Noise_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Burgert_Go-with-the-Flow_Motion-Controllable_Video_CVPR_2025_supplemental.pdf
| null |
@InProceedings{Burgert_2025_CVPR,
author = {Burgert, Ryan and Xu, Yuancheng and Xian, Wenqi and Pilarski, Oliver and Clausen, Pascal and He, Mingming and Ma, Li and Deng, Yitong and Li, Lingxiao and Mousavi, Mohsen and Ryoo, Michael and Debevec, Paul and Yu, Ning},
title = {Go-with-the-Flow: Motion-Controllable Video Diffusion Models Using Real-Time Warped Noise},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13-23}
}
|
Generative modeling aims to transform random noise into structured outputs. In this work, we enhance video diffusion models by allowing motion control via structured latent noise sampling. This is achieved by just a change in data: we pre-process training videos to yield structured noise. Consequently, our method is agnostic to diffusion model design, requiring no changes to model architectures or training pipelines. Specifically, we propose a novel noise warping algorithm, fast enough to run in real time, that replaces random temporal Gaussianity with correlated warped noise derived from optical flow fields, while preserving the spatial Gaussianity. The efficiency of our algorithm enables us to fine-tune modern video diffusion base models using warped noise with minimal overhead, and provide a one-stop solution for a wide range of user-friendly motion control: local object motion control, global camera movement control, and motion transfer. The harmonization between temporal coherence and spatial Gaussianity in our warped noise leads to effective motion control while maintaining per-frame pixel quality. Extensive experiments and user studies demonstrate the advantages of our method, making it a robust and scalable approach for controlling motion in video diffusion models. Please see our project webpage: https://eyeline-research.github.io/Go-with-the-Flow/ ; source code and checkpoints are available on GitHub: https://github.com/Eyeline-Research/Go-with-the-Flow
|
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] |
499 |
Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation
|
[
"Yuan Gan",
"Jiaxu Miao",
"Yunze Wang",
"Yi Yang"
] |
https://openaccess.thecvf.com/content/CVPR2025/html/Gan_Silence_is_Golden_Leveraging_Adversarial_Examples_to_Nullify_Audio_Control_CVPR_2025_paper.html
|
https://openaccess.thecvf.com/content/CVPR2025/papers/Gan_Silence_is_Golden_Leveraging_Adversarial_Examples_to_Nullify_Audio_Control_CVPR_2025_paper.pdf
|
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gan_Silence_is_Golden_CVPR_2025_supplemental.zip
| null |
@InProceedings{Gan_2025_CVPR,
author = {Gan, Yuan and Miao, Jiaxu and Wang, Yunze and Yang, Yi},
title = {Silence is Golden: Leveraging Adversarial Examples to Nullify Audio Control in LDM-based Talking-Head Generation},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
month = {June},
year = {2025},
pages = {13434-13444}
}
|
Advances in talking-head animation based on Latent Diffusion Models (LDM) enable the creation of highly realistic, synchronized videos. These fabricated videos are indistinguishable from real ones, increasing the risk of potential misuse for scams, political manipulation, and misinformation. Hence, addressing these ethical concerns has become a pressing issue in AI security. Recent proactive defense studies focused on countering LDM-based models by adding perturbations to portraits. However, these methods are ineffective at protecting reference portraits from advanced image-to-video animation. The limitations are twofold: 1) they fail to prevent images from being manipulated by audio signals, and 2) diffusion-based purification techniques can effectively eliminate protective perturbations. To address these challenges, we propose Silencer, a two-stage method designed to proactively protect the privacy of portraits. First, a nullifying loss is proposed to ignore audio control in talking-head generation. Second, we apply anti-purification loss in LDM to optimize the inverted latent feature to generate robust perturbations. Extensive experiments demonstrate the effectiveness of Silencer in proactively protecting portrait privacy. We hope this work will raise awareness among the AI security community regarding critical ethical issues related to talking-head generation techniques. Code: https://github.com/yuangan/Silencer.
|
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