[ACM MM 2021] TSA-Net: Tube Self-Attention Network for Action Quality Assessment

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Deep Learning TSA-Net
Overview

Tube Self-Attention Network (TSA-Net)

This repository contains the PyTorch implementation for paper TSA-Net: Tube Self-Attention Network for Action Quality Assessment (ACM-MM'21 Oral)

[arXiv] [supp] [slides] [poster] [video]

If this repository is helpful to you, please star it. If you find our work useful in your research, please consider citing:

@inproceedings{TSA-Net,
  title={TSA-Net: Tube Self-Attention Network for Action Quality Assessment},
  author={Wang, Shunli and Yang, Dingkang and Zhai, Peng and Chen, Chixiao and Zhang, Lihua},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  year={2021},
  pages={4902–4910},
  numpages={9}
}

User Guide

In this repository, we open source the code of TSA-Net on FR-FS dataset. The initialization process is as follows:

# 1.Clone this repository
git clone https://github.com/Shunli-Wang/TSA-Net.git ./TSA-Net
cd ./TSA-Net

# 2.Create conda env
conda create -n TSA-Net python
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
pip install -r requirements.txt

# 3.Download pre-trained model and FRFS dataset. All download links are listed as follow.
# PATH/TO/rgb_i3d_pretrained.pt 
# PATH/TO/FRFS 

# 4.Create data dir
mkdir ./data && cd ./data
mv PATH/TO/rgb_i3d_pretrained.pt ./
ln -s PATH/TO/FRFS ./FRFS

After initialization, please check the data structure:

.
├── data
│   ├── FRFS -> PATH/TO/FRFS
│   └── rgb_i3d_pretrained.pt
├── dataset.py
├── train.py
├── test.py
...

Download links:

Training & Evaluation

We provide the training and testing code of TSA-Net and Plain-Net. The difference between the two is whether the TSA module exists. This option is controlled by --TSA item.

python train.py --gpu 0 --model_path TSA-USDL --TSA
python test.py --gpu 0 --pt_w Exp/TSA-USDL/best.pth --TSA

python train.py --gpu 0 --model_path USDL
python test.py --gpu 0 --pt_w Exp/USDL/best.pth

Acknowledgement

Our code is adapted from MUSDL. We are very grateful for their wonderful implementation. All tracking boxes in our project are generated by SiamMask. We also sincerely thank them for their contributions.

Contact

If you have any questions about our work, please contact [email protected].

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Comments
  • The copyright of Olympic broadcast video

    The copyright of Olympic broadcast video

    Thanks for the great work.

    As far as I know, the Olympic broadcast video is copyrighted, is it legal to use this dataset in my work?

    Could you provide some clarification on copyright?

    Looking forward to your reply.

    opened by Lycan1003 0
  • clarification on tube generation

    clarification on tube generation

    Hi, Regarding the tube generation process in get_mask() in evaluator.py. As far as i can see the actor's bounding boxes are extracted using SiamMask on images of size 640x360 (FRFS) which are then passed to get_mask(). On the other hand the dataloader in get_imgs() first down scales frames to 455x256 followed by a crop of 224x224. From looking at get_mask() i see that lines 21-25 intend to place the actor's bounding box properly on the feature maps (spatially), but i am not certain they will fit spatially because the actors bounding boxes did not adjust to the same downscale and crop process that the frames did. Is this a bug or i perhaps i am misunderstanding something? Thanks

    opened by orikorner 1
Owner
ShunliWang
ShunliWang
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