README.md

December 25, 2025 · View on GitHub

Semi-Supervised Teacher-Reference-Student Architecture for Action Quality Assessment


arXiv GitHub
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia

Existing action quality assessment (AQA) methods often require a large number of label annotations for fully supervised learning, which are laborious and expensive. In practice, the labeled data are difficult to obtain because the AQA annotation process requires domain-specific expertise. In this paper, we propose a novel semi-supervised method, which can be utilized for better assessment of the AQA task by exploiting a large amount of unlabeled data and a small portion of labeled data. Differing from the traditional teacher-student network, we propose a teacher-reference-student architecture to learn both unlabeled and labeled data, where the teacher network and the reference network are used to generate pseudo-labels for unlabeled data to supervise the student network.

Release

  • 2025-05 💾 We released our code.
  • 2024-10 🚀 Our paper accepted by ECCV 2024.

Results

We utilize the Spearman’s rank correlation as an evaluation metric to assess the performance of our method under different labeled data.

Data Preparation

  1. Prepare MTL-AQA dataset.
    • Download the dataset from the link provided in this repo.
  2. Download the I3D backbone pretrained on Kinetics (this repo).
  3. Unzip it under the dataset/ folder.

Installation

  1. Dependencies
  • python == 3.8.13
  • torch== 1.12.1
  1. Create conda environment 
    conda create --name TRS python=3.8.13
source activate TRS
pip install -r requirements.txt

Training

Run the following code to start training.

python -u main.py --gpu 0 --exp 1

Acknowledgement

Our evaluation code is build upon USDL, CoRe. We acknowledge these team for their valuable contributions to the field of action quality assessment.

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{yun2025semi,
  title={Semi-Supervised Teacher-Reference-Student Architecture for Action Quality Assessment},
  author={Yun, Wulian and Qi, Mengshi and Peng, Fei and Ma, Huadong},
  booktitle={European Conference on Computer Vision},
  pages={161--178},
  year={2025},
  organization={Springer}
}