HankYang428/unictokens_data

UniCTokens Dataset

Version · 2025-10-24

1 Data Overview

Item	Description
Total concepts	20 (Human × 10 · Animal × 5 · Object × 5)
Images per concept	N ≈ 10 – 15 (already split into train / test)
Negative samples	random_images/ (100 random irrelevant images) + negative_example/ (hard negatives)

2 Benchmark Tasks

2.1 MMU (Multi-Modal Understanding)

Sub-task	Source files	Evaluation focus
Text-Only QA	test/<concept>/text_only.json	Check whether the model remembers concept knowledge (no image)
VQA	test/<concept>/vqa.json + image	Visual question answering about the concept image
Rec	test/*.png	Pure visual recognition capability

2.2 T2I (Text-to-Image Generation)

Mode	Input	Metrics
Vanilla generation	Prompts from the DreamBooth Dataset → target-concept images	CLIP-I / CLIP-T · ArcFace similarity
Personalized knowledge-driven	t2i_conditions.json	Combined T2I-Score: must satisfy both visual & textual attributes

3 Directory Structure

UniCTokens/
├── black_512x512.png          # Pure black placeholder
├── concepts_list.json         # List of 20 concept names
├── template.json              # Template for generating training data
├── random_images/             # 100 simple negative samples for training
│   ├── 0.png
│   └── … 99.png
├── concept/                   # 🔑 Concept data (train / test)
│   ├── train/
│   │   └── <concept_name>/    # 20 folders
│   │       ├── 0.png … N.png          # Original training images
│   │       ├── cropped/               # Cropped regions
│   │       ├── info.json              # Concept profile & extra info
│   │       ├── conversations.json     # Training dialogues
│   │       ├── positive_recognitions.json  # Positive QA pairs
│   │       ├── random_recognitions.json    # Negative QA pairs
│   │       └── negative_example/      # Hard negatives + score.json
│   └── test/
│       └── <concept_name>/
│           ├── 0.png … 4.png
│           ├── text_only.json         # Text-only QA
│           ├── vqa.json               # VQA pairs
│           └── t2i_conditions.json    # Conditions for knowledge-driven T2I
├── gen_showo_training_data.py   # Script to create Stage-1/2/3 training files
├── gen_test_data.py             # Script to create all evaluation files
└── README.md

4 Quick Start

1. Set the dataset root Open gen_showo_training_data.py and gen_test_data.py, change

   DATA_ROOT = "/path/to/UniCTokens_Dataset"
   

   to the actual dataset path.

2. Generate data

   # Create Stage-1/2/3 training samples
   python gen_showo_training_data.py
   
   # Create MMU & T2I evaluation samples
   python gen_test_data.py
   

5 License

The dataset is released under CC-BY-NC 4.0 and is intended for academic research only. Commercial use is not permitted.

6 Citation

@article{an2025unictokens,
  title={UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens},
  author={An, Ruichuan and Yang, Sihan and Zhang, Renrui and Shen, Zijun and Lu, Ming and Dai, Gaole and Liang, Hao and Guo, Ziyu and Yan, Shilin and Luo, Yulin and others},
  journal={arXiv preprint arXiv:2505.14671},
  year={2025}
}

7 Contact

• GitHub Issues: https://github.com/arctanxarc/UniCTokens/issues
• Email: [email protected]