Awesome

AS-CAL

Introduction

This is the official implementation of "Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition".

Requirements

• Python 3.6
• Pytorch 1.0.1

Datasets

• NTU RGB+D 60:
  Download raw data from https://github.com/shahroudy/NTURGB-D
  Use st-gcn/tools/ntu_gendata.py in https://github.com/yysijie/st-gcn to prepare data
• NTU RGB+D 120:
  Same as NTU RGB+D 60 but needs some modification for NTU RGB+D 120.
• SBU, UWA3D, N-UCLA
  Unzip the .zip file in /data and put them into the directory corresponding to the one in codes.

Usage

• pretrain and then linear evaluation:
  python pretrain_and_linEval.py

• reload pre-trained models and linear evaluation:
  python linEval.py --mode eval --model_path ./pretrained_model.pth

• supervised:
  python linEval.py --mode supervise

• reload pre-trained models and semi-supervised:
  python linEval.py --mode semi --model_path ./pretrained_model.pth

For more customized parameter settings, you can change them in parse_option() and/or parse_option_lin_eval(). But some parameters may be reset in the later lines. Carefully check. Apology！

Tips

To debug, we suggest to set epochs to 2 and save_freq to 1 in parse_option().
Besides, we suggest to set epochs to 1 in parse_option_lin_eval().

License

AS-CAL is released under the MIT License.

Citation

@article{RAO202190,
title = {Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition},
journal = {Information Sciences},
volume = {569},
pages = {90-109},
year = {2021},
doi = {https://doi.org/10.1016/j.ins.2021.04.023},
author = {Haocong Rao and Shihao Xu and Xiping Hu and Jun Cheng and Bin Hu},
}