README.md
August 19, 2025 ยท View on GitHub
CoLeaF: A Contrastive-Collaborative Learning Framework for Weakly Supervised Audio-Visual Video Parsing
Faegheh Sardari, Armin Mustafa, Philip J.B. Jacksonn, Adrian Hilton
Code for ECCV 2024 paper CoLeaF: A Contrastive-Collaborative Learning Framework for Weakly Supervised Audio-Visual Video Parsing
Prerequisites
- Linux
- Python 3
- CPU or NVIDIA GPU + CUDA CuDNN
Data Preparation
- Create a folder named 'features' inside the data folder
- Download the audio and visual features from https://github.com/YapengTian/AVVP-ECCV20 and transfer them to the 'features' folder.
Train & Test
Run main.py
Test our pretrianed model
Run main.py --mode test
Citation
If you use this code for your research, please cite our papers.
@article{sardari2024coleaf,
title={CoLeaF: A Contrastive-Collaborative Learning Framework for Weakly Supervised Audio-Visual Video Parsing},
author={Sardari, Faegheh and Mustafa, Armin and Jackson, Philip JB and Hilton, Adrian},
journal={European Conference on Computer Vision},
year={2024}
}
License
Copyright (C) 2024 University of Surrey. The code repository is published under the CC-BY-NC 4.0 license.
Acknowledgments
This repository includes the modified codes from:
- HAN (ECCV-2020) https://github.com/YapengTian/AVVP-ECCV20
- JoMoLD (ECCV-2022) https://github.com/MCG-NJU/JoMoLD
- CMPAE (CVPR-2023) https://github.com/MengyuanChen21/CVPR2023-CMPAE?tab=readme-ov-file shared under this license (https://github.com/MengyuanChen21/CVPR2023-CMPAE/blob/main/LICENSE)
We are grateful to the creators of these repositories.
This research is also supported by UKRI EPSRC Platform Grant EP/P022529/1, and EPSRC BBC Prosperity Partnership AI4ME: Future Personalised ObjectBased Media Experiences Delivered at Scale Anywhere EP/V038087/1.