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[NeurIPS 2024] CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition

CHASE 최신버젼 다운로드

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

Here's the official implementation of 
1. De-biasing Skeleton-based Action Recognition with Convex Hull Adaptive Shift (Under Review)
2. [CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition](https://arxiv.org/abs/2410.07153) accepted in [NeurIPS 2024](https://nips.cc/virtual/2024/poster/94816).

![](https://github.com/Necolizer/CHASE/blob/gh-pages/static/images/EntityBias.svg)

![](https://github.com/Necolizer/CHASE/blob/gh-pages/static/images/Viz.svg)

## 1. De-biasing Skeleton-based Action Recognition with Convex Hull Adaptive Shift

Detailed Implementation: See folder `JournalSubmission` [README 1](./JournalSubmission/CHASE-Joint/README.md) & [README 2](./JournalSubmission/CHASE-SubEntity/README.md)

## 2. [NeurIPS'24] CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition

Detailed Implementation: See folder `NeurIPS24` [README](./NeurIPS24/README.md)

To clone the `main` branch only (for code) and exclude the `gh-pages` branch (for project page), use the following `git` command:
```shell
git clone -b main https://github.com/Necolizer/CHASE.git
pip install -r requirements.txt
cd ./NeurIPS24
```

For datasets:
- Please refer to [ISTA-Net](https://github.com/Necolizer/ISTA-Net) and follow the instructions in section [Prepare the Datasets](https://github.com/Necolizer/ISTA-Net?tab=readme-ov-file#3-prepare-the-datasets) to prepare NTU Mutual 11 & 26, H2O, and Assembly101.
- Please refer to [COMPOSER](https://github.com/hongluzhou/composer) repo's section [Dataset Preparation](https://github.com/hongluzhou/composer?tab=readme-ov-file#dataset-preparation) to get Collective Activity and Volleyball. You could directly download the data using their provided google drive links.

To run the code:
```shell
python main.py --config config/[yourBackboneName]/[dataset]/[yourSetting]_chase.yaml
python main_group.py --config config/[yourBackboneName]/[cadORvol]/[yourSetting]_chase.yaml
```

Checkpoints of the best backbone for each benchmark are provided in this [Hugging Face repo](https://huggingface.co/Necolizer/CHASE).

## 3. Citation

If you find this work or code helpful in your research, please consider citing:
```
@inproceedings{NEURIPS2024_wen2024chase,
    author = {Wen, Yuhang and Liu, Mengyuan and Wu, Songtao and Ding, Beichen},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang},
    pages = {9388--9420},
    publisher = {Curran Associates, Inc.},
    title = {CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action Recognition},
    url = {https://proceedings.neurips.cc/paper_files/paper/2024/file/11f5520daf9132775e8604e89f53925a-Paper-Conference.pdf},
    volume = {37},
    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}
}
```

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