双分支引导对比学习的无监督行人重识别

任航佳; 梁凤梅

doi:10.11959/j.issn.1000-0801.2025125

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双分支引导对比学习的无监督行人重识别

研究与开发 | 更新时间：2025-06-28

- 双分支引导对比学习的无监督行人重识别
- Dual branch guided contrastive learning for unsupervised pedestrian re-identification
- 电信科学 2025年41卷第6期页码：92-102
- 作者机构：
  
  太原理工大学电子信息工程学院，山西晋中 030600
- 作者简介：
  
  [ "任航佳（1998- ），男，太原理工大学电子信息工程学院硕士生，主要研究方向为深度学习、计算机视觉和行人重识别。" ]
  [ "梁凤梅（1969- ），女，博士，太原理工大学电子信息工程学院副教授、硕士生导师，主要研究方向为图像处理与图像通信、计算机视觉和智能信息处理。" ]
- 基金信息：
  
  虚拟现实技术与系统全国重点实验室开放课题基金项目(VRLAB2023A06);山西省科技合作交流专项(202104041101030)
- DOI：10.11959/j.issn.1000-0801.2025125
  中图分类号： TP391
- 收稿：2024-12-14，
  
  修回：2025-04-28，
  
  纸质出版：2025-06-20
- 稿件说明：
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任航佳,梁凤梅.双分支引导对比学习的无监督行人重识别[J].电信科学,2025,41(06):92-102.

REN Hangjia,LIANG Fengmei.Dual branch guided contrastive learning for unsupervised pedestrian re-identification[J].Telecommunications Science,2025,41(06):92-102.
任航佳,梁凤梅.双分支引导对比学习的无监督行人重识别[J].电信科学,2025,41(06):92-102. DOI： 10.11959/j.issn.1000-0801.2025125.

REN Hangjia,LIANG Fengmei.Dual branch guided contrastive learning for unsupervised pedestrian re-identification[J].Telecommunications Science,2025,41(06):92-102. DOI： 10.11959/j.issn.1000-0801.2025125.

摘要

现有无监督行人重识别算法使用残差网络，仅能提取粗略的全局特征，对细微的局部特征反映不足，且聚类方法生成的伪标签会引入噪声，影响特征判别。针对上述问题，提出一种双分支引导对比学习的方法。首先，引入一种有效的特征提取方式，将提取的特征分为全局分支和局部分支，提高对局部信息的利用；其次，通过全局特征和局部特征之间的一致性细化全局特征预测的伪标签，充分利用局部特征和整体特征之间的互补关系，有效降低伪标签聚类产生的噪声；最后，引入对比学习模块，将细化的标签进行对比学习，提高模型的鲁棒性。在Market1501、DukeMTMC-ReID以及MSMT17数据集上的实验结果验证了所提方法的有效性及高性能。

Abstract

The current unsupervised pedestrian re-identification algorithms using residual networks can only extract rough global features

but it can’t adequately reflect subtle local features. In addition

the pseudo labels generated by clustering methods introduce noise

which will affect the performance of feature discrimination. A dual branch guided contrastive learning method was proposed. Firstly

an effective feature extraction method was introduced

which divided the extracted features into global branches and local branches to improve the utilization of local information. Secondly

the consistency between global and local features was proposed to refine the pseudo labels for global feature prediction

utilizing the complementary relationship between local and global features

thereby effectively reducing the noise generated by pseudo label clustering. Finally

a contrastive learning module was proposed to perform contrastive learning on refined labels and improve the robustness of the model. The experimental results on the Market1501

DukeMTMC-ReID

and MSMT17 datasets validate the effectiveness of the proposed method.

关键词

Keywords

references

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