Awesome
PCRP
Introduction
This is the official implementation of "Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton based Action Recognition".
Requirements
- Python 3.6
- Pytorch 1.0.1
Datasets
- N-UCLA:
Download transformed data from https://github.com/shlizee/Predict-Cluster/tree/master/ucla_github_pytorch/UCLAdata - NTU RGB+D 60:
Download transformed data from https://github.com/shlizee/Predict-Cluster. - NTU RGB+D 120:
Download raw data from https://github.com/shahroudy/NTURGB-D.
Usentu_gendata_for_predictCluster_right.py
to reprocess raw data for view invariant transformation.
Put the data into the folder that matches the codes in pc_test.py
Usage
- pretrain and then linear evaluation:
python pc_test.py
License
PCRP is released under the MIT License.
Citation
@misc{xu2020prototypical,
title={Prototypical Contrast and Reverse Prediction: Unsupervised Skeleton Based Action Recognition},
author={Shihao Xu and Haocong Rao and Xiping Hu and Bin Hu},
year={2020},
eprint={2011.07236},
archivePrefix={arXiv},
primaryClass={cs.CV}
}