DPRN

This repository provides a PyTorch implementation of the paper Maximization and restoration: Action segmentation through dilation passing and temporal reconstruction.

Tested with:

• PyTorch 0.4.1
• Python 2.7.12

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 python main.py --action=train --dataset=DS --split=SP where DS is breakfast, 50salads or gtea, and SP is the split number (1-5) for 50salads and (1-4) for the other datasets.

Prediction:

Run python main.py --action=predict --dataset=DS --split=SP.

Evaluation:

Run python eval.py --dataset=DS --split=SP.

Citation:

If you use the code, please cite

Park, Junyong, et al. "Maximization and restoration: Action segmentation through dilation passing and temporal reconstruction." Pattern Recognition 129 (2022): 108764.

Acknowledgement

The repository of MS-TCN has been used for the general structure of this project