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CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition

arXiv PDF project page license

Here's the official implementation of CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition accepted in NeurIPS 2024.

0. Table of Contents

1. Change Log

  • [2024/10/11] Our paper is available in arXiv. Visit our project website!
  • [2024/09/27] This work is accepted by NeurIPS 2024. We make our scripts and checkpoints public.

2. Prerequisites

To clone the main branch only (for code) and exclude the gh-pages branch (for project website), use the following git command:

git clone -b main https://github.com/Necolizer/CHASE.git
pip install -r requirements.txt

3. Datasets

3.1 NTU Mutual 11 & 26, H2O, Assembly101

Please refer to ISTA-Net and follow the instructions in section Prepare the Datasets to prepare these datasets.

3.2 Collective Activity, Volleyball

Please refer to COMPOSER repo's section Dataset Preparation. You could directly download the data using their provided google drive links.

4. Run the Code

4.1 NTU Mutual 11, NTU Mutual 26

python main.py --config config/[yourBackboneName]/[ntu11ORntu26]/[yourSetting]_chase.yaml

4.2 H2O

Train & Validate

python main.py --config config/[yourBackboneName]/h2o/h2o_chase.yaml

Generate JSON File for Test Result Submission

python main.py --config config/[yourBackboneName]/h2o/h2o_get_test_results_chase.yaml --weights path/to/your/checkpoint

Submit zipped json file action_labels.json in CodaLab Challenge H2O - Action to get the test accuracy scores.

4.3 Assembly101

Train & Validate

# Action (mandatory): 1380 classes
python main.py --config config/[yourBackboneName]/asb/asb_action_chase.yaml

Generate JSON File for Test Result Submission

# Action (mandatory): 1380 classes
python main.py --config config/[yourBackboneName]/asb/asb_action_get_test_results_chase.yaml --weights path/to/your/action/checkpoint

Submit zipped json file preds.json in CodaLab Challenge Assembly101 3D Action Recognition to get the test accuracy scores.

ATTENTION: preds.json for 'Action' is about 673Mb before compression.

4.4 Collective Activity, Volleyball

python main_group.py --config config/[yourBackboneName]/[cadORvol]/[yourSetting]_chase.yaml

5. Checkpoints

Checkpoints of the best backbone for each benchmark are provided in this Hugging Face repo.

6. Acknowledgement

Grateful to the authors of CTR-GCN, InfoGCN, STTFormer, HD-GCN, ISTA-Net, COMPOSER repository. Thanks to the authors for their great work.

7. Citation

If you find this work or code helpful in your research, please consider citing:

@inproceedings{wen2024chase,
    title={CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition},
    author={Yuhang Wen and Mengyuan Liu and Songtao Wu and Beichen Ding},
    booktitle={Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS)},
    year={2024},
}

@INPROCEEDINGS{wen2023interactive,
    author={Wen, Yuhang and Tang, Zixuan and Pang, Yunsheng and Ding, Beichen and Liu, Mengyuan},
    booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
    title={Interactive Spatiotemporal Token Attention Network for Skeleton-Based General Interactive Action Recognition}, 
    year={2023},
    pages={7886-7892},
    doi={10.1109/IROS55552.2023.10342472}
}