Multi-Domain Learning and Identity Mining for Vehicle Re-Identification
Abstract
This paper introduces our solution for the Track2 in AI City Challenge 2020 (AICITY20). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data.Our solution is based on a strong baseline with bag of tricks (BoT-BS) proposed in person ReID. At first, we propose a multi-domain learning method to joint the real-world and synthetic data to train the model. Then, we propose the Identity Mining method to automatically generate pseudo labels for a part of the testing data, which is better than the k-means clustering. The tracklet-level re-ranking strategy with weighted features is also used to post-process the results. Finally, with multiple-model ensemble, our method achieves 0.7322 in the mAP score which yields third place in the competition. The codes are available at https://github.com/heshuting555/AICITY2020_DMT_VehicleReID.
Cite
Text
He et al. "Multi-Domain Learning and Identity Mining for Vehicle Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020. doi:10.1109/CVPRW50498.2020.00299Markdown
[He et al. "Multi-Domain Learning and Identity Mining for Vehicle Re-Identification." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020.](https://mlanthology.org/cvprw/2020/he2020cvprw-multidomain/) doi:10.1109/CVPRW50498.2020.00299BibTeX
@inproceedings{he2020cvprw-multidomain,
title = {{Multi-Domain Learning and Identity Mining for Vehicle Re-Identification}},
author = {He, Shuting and Luo, Hao and Chen, Weihua and Zhang, Miao and Zhang, Yuqi and Wang, Fan and Li, Hao and Jiang, Wei},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2020},
pages = {2485-2493},
doi = {10.1109/CVPRW50498.2020.00299},
url = {https://mlanthology.org/cvprw/2020/he2020cvprw-multidomain/}
}