Multi-branch Cooperative Network for Person Re-identification

被引：0
|
作者：
Zhang L. [1 ]
Wu X. [1 ]
Zhang S. [2 ]
Yin Z. [1 ]
机构：
[1] College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing
[2] School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Deep Learning; Feature Representation; Multi-branch Network Architecture; Person Re-identification;
D O I：
10.16451/j.cnki.issn1003-6059.202109008
中图分类号：
TB18 [人体工程学]; Q98 [人类学];
学科分类号：
030303 ; 1201 ;
摘要：
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. © 2021, Science Press. All right reserved.
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收藏
页码：853 / 862
页数：9
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