Awesome
Progressive Learning for Person Re-Identification with One Example
Pytorch implementation for our paper [Link]. This code is based on the Open-ReID library and Exploit-Unknown-Gradually.
Preparation
Dependencies
- Python 3.6
- PyTorch (version >= 0.4.1)
- scikit-learn, metric-learn, tqdm
Download datasets
- DukeMTMC-VideoReID: This page contains more details and baseline code.
- MARS: [Google Drive] [BaiduYun].
- Market-1501: [Direct Link] [Google Drive]
- DukeMTMC-reID: [Direct Link] [Google Drive]
- Move the downloaded zip files to
./data/and unzip here.
Train
sh run.sh
Please set the max_frames smaller if your GPU memory is less than 11G.
Performances
The performances varies according to random splits for initial labeled data. To reproduce the performances in our paper, please use the one-shot splits at ./examples/
Citation
Please cite the following papers in your publications if it helps your research:
@article{wu2019progressive,
title = {Progressive Learning for Person Re-Identification with One Example},
author = {Wu, Yu and Lin, Yutian and Dong, Xuanyi and Yan, Yan and Bian, Wei and Yang, Yi},
journal= {IEEE Transactions on Image Processing},
year = {2019},
volume = {28},
number = {6},
pages = {2872-2881},
doi = {10.1109/TIP.2019.2891895},
ISSN = {1057-7149},
month = {June},
}
@inproceedings{wu2018cvpr_oneshot,
title = {Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning},
author = {Wu, Yu and Lin, Yutian and Dong, Xuanyi and Yan, Yan and Ouyang, Wanli and Yang, Yi},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2018}
}
Contact
To report issues for this code, please open an issue on the issues tracker.
If you have further questions about this paper, please do not hesitate to contact me.