UniSoccer: Towards Universal Soccer Video Understanding

This repository contains the official PyTorch implementation of UniSoccer: https://arxiv.org/abs/2412.01820/.

The code, data, and checkpoints will be released soon... We are working on it.

Some Information

Project Page $\cdot$ Paper $\cdot$ Dataset(Soon) $\cdot$ Checkpoints(Soon)

News

• [2024.12] Our pre-print paper is released on arXiv.

Requirements

• Python >= 3.8 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 2.0.0 (If use A100)
• transformers >= 4.42.3
• pycocoevalcap >= 1.2

A suitable conda environment named UniSoccer can be created and activated with:

conda env create -f environment.yaml
conda activate UniSoccer

Train

To be updated soon...

Inference

To be updated soon...

Citation

If you use this code and data for your research or project, please cite:

@misc{rao2024unisoccer,
        title   = {Towards Universal Soccer Video Understanding},
        author  = {Rao, Jiayuan and Wu, Haoning and Jiang, Hao and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
        journal = {arXiv preprint arXiv:2412.01820},
        year    = {2024},
  }

TODO

• Release Paper
• Release Checkpoints
• Release Dataset
• Code of Visual Encoder Pretraining
• Code of Downstream Tasks
• Code of Inference
• Code of Evaluation

Acknowledgements

Many thanks to the code bases from Video-LLaMA and MatchTime, and source data from SoccerNet-Caption and MatchTime.

Contact

If you have any questions, please feel free to contact jy_rao@sjtu.edu.cn or haoningwu3639@gmail.com.