Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification" If you find this help your research, please cite

@inproceedings{DBLP:conf/cvpr/YanQC0ZT020,
  author    = {Yichao Yan and
               Jie Qin and
               Jiaxin Chen and
               Li Liu and
               Fan Zhu and
               Ying Tai and
               Ling Shao},
  title     = {Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification},
  booktitle = {2020 {IEEE/CVF} Conference on Computer Vision and Pattern Recognition,
               {CVPR} 2020, Seattle, WA, USA, June 13-19, 2020},
  pages     = {2896--2905},
  publisher = {{IEEE}},
  year      = {2020}
}

We use python 3.7 and pytorch=0.4

All experiments are done on MARS, as it is the largest dataset available to date for video-based person reID. Please follow deep-person-reid to prepare the data. The instructions are copied here:

1. Create a directory named mars/ under data/.
2. Download dataset to data/mars/ from http://www.liangzheng.com.cn/Project/project_mars.html.
3. Extract bbox_train.zip and bbox_test.zip.
4. Download split information from https://github.com/liangzheng06/MARS-evaluation/tree/master/info and put info/ in data/mars (we want to follow the standard split in [8]). The data structure would look like:

mars/
    bbox_test/
    bbox_train/
    info/

To train the model, please run

sh run_hypergraphsage_part.sh

Trained model [Google]

The shared trained model achieves 85.6% mAP and 89.5% rank-1 accuracy. According to my training log, the best model achieves 86.2% mAP and 90.0% top-1 accuracy. This may need adjustion in hyperparameters.

Our code is developed based on Video-Person-ReID (https://github.com/jiyanggao/Video-Person-ReID).