重庆工商大学学报（自然科学版）2024，Vol.41Issue(4)：77-85,9.DOI:10.16055/j.issn.1672-058X.2024.0004.010
多分支融合变分细化蒸馏的跨模态行人重识别
Cross-modal Person Re-identification Based on Multi-branch Fusion Variational Refinement Distillation
摘要
Abstract
Objective Aiming at the problem of insufficient attention to the delicate area of pedestrians and the vulnerability of the network to noise in the current cross-modal person re-identification research,this paper proposed a multi-branch fusion variational refinement distillation learning method.Methods Firstly,the network aggregated global features of different granularity through multiple branches,urging the deep network to learn the global information and details of the two modes to enrich the feature descriptors of pedestrians.Then,combined with the variational refinement distillation strategy,the feature information was recompressed,the deep information related to the task was retained,and the useless interferences were discarded.Finally,the different features captured by the network were jointly supervised by multiple loss functions to improve the sensitivity of the network to pedestrian representation.Results R-1 and mAP reached 66.93％and 65.25％,respectively,with the proposed method in the full search mode of the SYSU-MM01 dataset;the R-1 and mAP reached 78.26％and 77.83％respectively in the visible to infrared setting of the RegDB dataset.Conclusion Through ablation experiments,comparative experiments,and visualization experiments,the effectiveness of the proposed method is fully verified.关键词
行人重识别/跨模态/多分支聚合/变分细化蒸馏/多损失Key words
person re-identification/cross-modality/multi-branch aggregation/variational refinement distillation/multiple losses分类
计算机与自动化引用本文复制引用
王路遥,王凤随,陈元妹..多分支融合变分细化蒸馏的跨模态行人重识别[J].重庆工商大学学报（自然科学版）,2024,41(4):77-85,9.基金项目
安徽省自然科学基金(2108085MF197,1708085MF154) （2108085MF197,1708085MF154）
安徽高校省级自然科学研究重点项目(KJ2019A0162) （KJ2019A0162）
检测技术与节能装置安徽省重点实验室开放基金资助项目(DTESD2020B02) （DTESD2020B02）
安徽工程大学国家自然科学基金预研项目(XJKY2022040) （XJKY2022040）
安徽高校研究生科学研究项目(YJS20210448,YJS20210449). （YJS20210448,YJS20210449）
