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Branch-Cooperative OSNet for Person Re-Identification

2020-06-12 14:09:23
Lei Zhang, Xiaofu Wu, Suofei Zhang, Zirui Yin
arXiv_CV
         
Abstract

Multi-branch is extensively studied for learning rich feature representation for person re-identification (Re-ID). In this paper, we propose a branch-cooperative architecture over OSNet, termed BC-OSNet, for person Re-ID. By stacking four cooperative branches, namely, a global branch, a local branch, a relational branch and a contrastive branch, we obtain powerful feature representation for person Re-ID. Extensive experiments show that the proposed BC-OSNet achieves state-of-art performance on the three popular datasets, including Market-1501, DukeMTMC-reID and CUHK03. In particular, it achieves mAP of 84.0% and rank-1 accuracy of 87.1% on the CUHK03_labeled.

Abstract (translated)

URL

https://arxiv.org/abs/2006.07206

PDF

https://arxiv.org/pdf/2006.07206.pdf

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