  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.
