HGCN_AQA

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Implementation of HGCN for AQA

HGCN_AQA 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# HGCN

This is the implementation for *Hierarchical Graph Convolutional Networks for Action Quality Assessment*, which has been accepted by IEEE TCSVT.  The framework overview is as follows:

![](https://raw.githubusercontent.com/ZhouKanglei/HGCN_AQA/main/imgs/overview.png)

<center>We propose a hierarchical graph convolutional network (HGCN) that corrects semantic information confusion, constructs meaningful scenes from consecutive shots, and performs sequential action aggregation to enhance discriminative features and improve assessment performance in action quality assessment.</center>

[Paper address](https://doi.org/https://doi.org/10.1109/TCSVT.2023.3281413) | [Project page](http://hubertshum.com/pbl_tcsvt2023aqa.htm)

## Requirements

- We provide the [dependency file](requirements.txt) of our experimental environment, one can install all dependencies by creating a new anaconda virtual environment and running:

````bash
pip install -r requirements.txt
````

- torch_videovision

```bash
pip install git+https://github.com/hassony2/torch_videovision
```

## Datasets

- MTL-AQA: Download the dataset from [the repository](https://github.com/ParitoshParmar/MTL-AQA). The data structure is:

```bash
$DATASET_ROOT
├── MTL-AQA/
    ├── new
        ├── new_total_frames_256s
            ├── 01
            ...
            └── 09
    ├── info
        ├── final_annotations_dict_with_dive_number
        ├── test_split_0.pkl
        └── train_split_0.pkl
    └── model_rgb.pth
```

- AQA-7: Download AQA-7 Dataset using the following commands:
```bash
mkdir AQA-Seven & cd AQA-Seven
wget http://rtis.oit.unlv.edu/datasets/AQA-7.zip
unzip AQA-7.zip
```
The data structure is:

```bash
$DATASET_ROOT
├── Seven/
    ├── diving-out
        ├── 001
            ├── img_00001.jpg
            ...
        ...
        └── 370
    ├── gym_vault-out
        ├── 001
            ├── img_00001.jpg
            ...
    ...

└── Split_4
        ├── split_4_test_list.mat
        └── split_4_train_list.mat
```

- JIGSAWS: Download the dataset from the [website](https://cs.jhu.edu/~los/jigsaws/info.php).

## Training and Evaluation

The command helper is:

```bash
usage: main.py [-h] [--benchmark {MTL,Seven,JIGSAWS}] [--exp_name EXP_NAME]
               [--fix_bn FIX_BN] [--resume RESUME] [--ckpts CKPTS]
               [--class_idx {1,2,3,4,5,6}] [--use_gpu USE_GPU]
               [--device DEVICE] [--seed SEED] [--model MODEL] [--phase PHASE]
               [--config CONFIG] [--weight_decay WEIGHT_DECAY]
               [--base_lr BASE_LR] [--fold FOLD] [--bs_train BS_TRAIN]
               [--bs_test BS_TEST] [--num_groups NUM_GROUPS]
```

### Training from scratch

One can use the following command to train the model from scratch:

```bash
python main.py --benchmark MTL/Seven --phase train \
    --model models.hgcn --device 01 --exp_name default \
    --num_groups 1/2/4/8 --ckpts weights/MTL.pth
```

### Evaluation using the pretrained model

We provide the pretrained model at [Google drive](https://drive.google.com/drive/folders/1P6ZgE0DksnWBw4oJFg-vo5kY0SsWD1G_?usp=sharing). Please download it into the `./weights` folder.  One can use the following command to run the example code:

```bash
python main.py --benchmark MTL --phase test \
    --model models.hgcn --device 01 \
    --num_groups 8 --ckpts weights/MTL-group8.pth
```

## Citation

If you find our work useful in your research, please consider citing:

```latex
@article{zhou23hierarchical,
    author={Zhou, Kanglei and Ma, Yue and Shum, Hubert P. H. and Liang, Xiaohui},
    journal={IEEE Transactions on Circuits and Systems for Video Technology},
    title={Hierarchical Graph Convolutional Networks for Action Quality Assessment},
    year={2023},
    doi={10.1109/TCSVT.2023.3281413},
    issn={1051-8215},
    publisher={IEEE},
}
```

## Contact

For any questions, feel free to contact me via `zhoukanglei@qq.com`.

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