[CVPR-2021] UnrealPerson: An adaptive pipeline for costless person re-identification
In our paper (arxiv), we propose a novel pipeline, UnrealPerson, that decreases the costs in both the training and deployment stages of person ReID.
We develop an automatic data synthesis toolkit and use synthesized data in mutiple ReID tasks, including (i) Direct transfer, (ii) Unsupervised domain adaptation, and (iii) Supervised fine-tuning.
This repo contains:
Highlights:
If you find our work useful in your research, please kindly cite:
@inproceedings{zhang2021unrealperson,
title={UnrealPerson: An Adaptive Pipeline towards Costless Person Re-identification},
author={Tianyu Zhang and Lingxi Xie and Longhui Wei and Zijie Zhuang and Yongfei Zhang and Bo Li and Qi Tian},
year={2021},
booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}
}
If you have any questions about the data or paper, please leave an issue or contact me: [email protected]