Branch-Cooperative OSNet for Person Re-Identification
This work addresses person re-identification for surveillance and security applications, presenting an incremental improvement over existing multi-branch methods.
The paper tackled person re-identification by proposing BC-OSNet, a branch-cooperative architecture that stacks four branches to learn rich feature representations, achieving state-of-the-art performance with 84.0% mAP and 87.1% rank-1 accuracy on CUHK03_labeled.
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.