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--- |
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license: apache-2.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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--- |
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# \[NeurIPS 2024\] CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition |
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ArXiv: https://arxiv.org/abs/2410.07153 |
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Github: https://github.com/Necolizer/CHASE |
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Checkpoints of best backbone (+CHASE) for each benchmark: |
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- NTU Mutual 11 (XSub): STSA-Net (+CHASE) |
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- NTU Mutual 11 (XView): CTR-GCN (+CHASE) |
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- NTU Mutual 26 (XSub): InfoGCN (+CHASE) |
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- NTU Mutual 26 (XSet): InfoGCN (+CHASE) |
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- H2O: STSA-Net (+CHASE) |
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- Assembly101 (Action): CTR-GCN (+CHASE) |
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- Collective Activity: CTR-GCN (+CHASE) |
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- Volleyball (Original): CTR-GCN (+CHASE) |
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## Citation |
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``` |
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@inproceedings{NEURIPS2024_wen2024chase, |
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author = {Wen, Yuhang and Liu, Mengyuan and Wu, Songtao and Ding, Beichen}, |
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booktitle = {Advances in Neural Information Processing Systems}, |
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editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang}, |
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pages = {9388--9420}, |
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publisher = {Curran Associates, Inc.}, |
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title = {CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition}, |
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url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/11f5520daf9132775e8604e89f53925a-Paper-Conference.pdf}, |
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volume = {37}, |
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year = {2024} |
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} |
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``` |
