基于多分支协作的行人重识别网络

Multi-branch Cooperative Network for Person Re-identification

设计多分支网络以提取分集特征已成为行人重识别领域的重要方向之一.由于单分支学习到的特征表达能力有限,所以文中提出基于多分支协作的行人重识别网络.在局部分支、全局分支、全局对比池化分支、关联分支这4个相互协作的分支上进行特征提取,获得强大的分集行人特征表达能力.文中网络可应用于不同的主干网络.实验中考虑OSNet、ResNet作为文中网络的主干网络进行验证.实验表明,文中网络在行人重识别数据集上均取得Start-of-the-art结果.

Designing multi-branch networks to learn rich feature representation is one of the important directions in person re-identification(Re-ID).Aiming at the limited feature representation learned by a single branch,a multi-branch cooperative network for person Re-ID(BC-Net)is proposed.Powerful feature representation for person Re-ID is obtained by extracting features from four cooperative branches,local branch,global branch,relational branch and contrastive branch.The proposed network can be applied to different backbone networks.OSNet and ResNet are considered as the backbone of the proposed network for verification.Extensive experiments show that BC-Net achieves state-of-the-art performance on the popular Re-ID datasets.