Self-supervised recalibration network for person re-identification  

作　　者：Shaoqi Hou Zhiming Wang Zhihua Dong Ye Li Zhiguo Wang Guangqiang Yin Xinzhong Wang 

机构地区：[1]Shenzhen Institute of Information Technology,Shenzhen,518172,China [2]University of Electronic Science and Technology of China,Chengdu,611731,China [3]Kash Institute of Electronics and Information Industry,Kashi,844099,China

出　　处：《Defence Technology（防务技术）》2024年第1期163-178,共16页Defence Technology

基　　金：supported in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B186 and No.2022D01B05)。

摘　　要：The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

关 键 词：Person re-identification Attention mechanism Global information Local information Adaptive weighted fusion 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] E91[自动化与计算机技术—计算机科学与技术]