Awesome

This is the official PyTorch implementation of our paper: Adversarial Bipartite Graph Learning for Video Domain Adaptation

Requirements

• Python 3.7, PyTorch 1.2, CUDA 10.2

Datasets

Experiments are conducted on four datasets: UCF-HMDB_small, UCF-HMDB_full, UCF-Olympic, Kinetics-Gamplay.

The downloaded files need to store in ./dataset.

Pre-extracted features and data lists can be downloaded as,

• Features

  • UCF: download
  • HMDB: download
  • Olympic: training | validation

• Data lists

  • UCF-Olympic
    • UCF: training list | validation list
    • Olympic: training list | validation list
  • UCF-HMDB_small
    • UCF: training list | validation list
    • HMDB: training list | validation list
  • UCF-HMDB_full
    • UCF: training list | validation list
    • HMDB: training list | validation list

• Kinetics-Gameplay: please fill this form to request the features and data lists. <br> <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a> The Kinetics-Gameplay dataset is licensed under <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA 4.0</a> for non-commercial purposes only.

Usage

• training/validation: Run ./script_<DATASET_NAME>_G.sh E.g., script_HMDB_Ucf_G.sh

Citation

If you find this repository useful, please cite our papers:

@inproceedings{DBLP:conf/mm/LuoHW0B20,
  author    = {Yadan Luo and
               Zi Huang and
               Zijian Wang and
               Zheng Zhang and
               Mahsa Baktashmotlagh},
  editor    = {Chang Wen Chen and
               Rita Cucchiara and
               Xian{-}Sheng Hua and
               Guo{-}Jun Qi and
               Elisa Ricci and
               Zhengyou Zhang and
               Roger Zimmermann},
  title     = {Adversarial Bipartite Graph Learning for Video Domain Adaptation},
  booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia, Virtual
               Event / Seattle, WA, USA, October 12-16, 2020},
  pages     = {19--27},
  publisher = {{ACM}},
  year      = {2020},
  url       = {https://doi.org/10.1145/3394171.3413897},
  doi       = {10.1145/3394171.3413897}
}