Multi-branch Cooperative Network for Person Re-identification
ZHANG Lei1, WU Xiaofu1, ZHANG Suofei2, YIN Zirui1
1. College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003 2. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003
Abstract 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.
Fund:National Natural Science Foundation of China(No.61701252)
Corresponding Authors:
WU Xiaofu, Ph.D., professor. His research interests include information theory and coding, machine learning, computer vision, information security and cryptology.
About author:: ZHANG Lei, master student. His research interests include person re-identification. ZHANG Suofei, Ph.D., lecturer. His research interests include image and video signal processing, machine learning and Internet of things. YIN Zirui, master student. His research interests include machine learning.
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2021, Vol. 34 
