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MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation (TPAMI 2020)

This repository provides a PyTorch implementation of the paper MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation.

Environment

Python3, pytorch

Training:

• Download the data folder, which contains the features and the ground truth labels. (~30GB) (If you cannot download the data from the previous link, try to download it from here)
• Extract it so that you have the data folder in the same directory as main.py.
• To train the model run sh train.sh ${dataset} ${split} where ${dataset} is breakfast, 50salads or gtea, and ${split} is the split number (1-5) for 50salads and (1-4) for the other datasets.

Evaluation

Run sh test_epoch.sh ${dataset} ${split} ${test_epoch}.

Cite:

@article{li2020ms,
   author={Shi-Jie Li and Yazan AbuFarha and Yun Liu and Ming-Ming Cheng and Juergen Gall},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
    title={MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation}, 
    year={2020},
    volume={},
    number={},
    pages={1-1},
    doi={10.1109/TPAMI.2020.3021756},
}

@inproceedings{farha2019ms,
  title={Ms-tcn: Multi-stage temporal convolutional network for action segmentation},
  author={Farha, Yazan Abu and Gall, Jurgen},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3575--3584},
  year={2019}
}