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Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19

Note

PyTorch version should be 0.3! For PyTorch0.4 or higher, the codes need to be modified.
Now we have updated the code to >=Pytorch0.4.
A new model named AAGCN is added, which can achieve better performance.

Data Preparation

Training & Testing

Change the config file depending on what you want.

`python main.py --config ./config/nturgbd-cross-view/train_joint.yaml`

`python main.py --config ./config/nturgbd-cross-view/train_bone.yaml`

To ensemble the results of joints and bones, run test firstly to generate the scores of the softmax layer.

`python main.py --config ./config/nturgbd-cross-view/test_joint.yaml`

`python main.py --config ./config/nturgbd-cross-view/test_bone.yaml`

Then combine the generated scores with:

`python ensemble.py` --datasets ntu/xview
 

Citation

Please cite the following paper if you use this repository in your reseach.

@inproceedings{2sagcn2019cvpr,  
      title     = {Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition},  
      author    = {Lei Shi and Yifan Zhang and Jian Cheng and Hanqing Lu},  
      booktitle = {CVPR},  
      year      = {2019},  
}

@article{shi_skeleton-based_2019,
    title = {Skeleton-{Based} {Action} {Recognition} with {Multi}-{Stream} {Adaptive} {Graph} {Convolutional} {Networks}},
    journal = {arXiv:1912.06971 [cs]},
    author = {Shi, Lei and Zhang, Yifan and Cheng, Jian and LU, Hanqing},
    month = dec,
    year = {2019},
}

Contact

For any questions, feel free to contact: lei.shi@nlpr.ia.ac.cn