Awesome
DiffAct
Code for Diffusion Action Segmentation (ICCV 2023).
Setup
- Recommended Environment: Python 3.9.2, Cuda 11.4, PyTorch 1.10.0
- Install dependencies:
pip3 install -r requirements.txt
Data
- Download features of 50salads, GTEA and Breakfast provided by MS-TCN and ASFormer: [Link1] [Link2]
- Unzip the data, rename it to "datasets" and put into the current directory
DiffAct/
├── datasets
│ ├── 50salads
│ │ ├── features
│ │ ├── groundTruth
│ │ ├── mapping.txt
│ │ └── splits
│ ├── breakfast
│ │ ├── features
│ │ ├── groundTruth
│ │ ├── mapping.txt
│ │ └── splits
│ └── gtea
│ ├── features
│ ├── groundTruth
│ ├── mapping.txt
│ └── splits
├── main.py
├── model.py
└── ...
Run
- Generate config files by
python3 default_configs.py
- Simply run
python3 main.py --config configs/some_config.json --device gpu_id
- Trained models and logs will be saved in the
result
folder
Trained Models
- We provide some trained models in the
trained_models
folder
Citation
@inproceedings{liu2023diffusion,
title={Diffusion Action Segmentation},
author={Liu, Daochang and Li, Qiyue and Dinh, Anh-Dung and Jiang, Tingting and Shah, Mubarak and Xu, Chang},
booktitle={International Conference on Computer Vision (ICCV)},
year={2023}
}
License
MIT