Unsupervised Domain Adaptive Person Re-Identification Based on Progressive Hybrid Contrastive Learning

ZHAO Yu; SHU Qiao-yuan

doi:10.12263/DZXB.20250110

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Unsupervised Domain Adaptive Person Re-Identification Based on Progressive Hybrid Contrastive Learning

Large-Scale Models and the Internet | 更新时间：2025-10-16

- Unsupervised Domain Adaptive Person Re-Identification Based on Progressive Hybrid Contrastive Learning
- ACTA ELECTRONICA SINICA Vol. 53, Issue 6, Pages: 1829-1846(2025)
- 作者机构：
  
  1.重庆第二师范学院人工智能学院，重庆 400065
  2.重庆第二师范学院数学与大数据学院，重庆 400065
- 作者简介：
- 基金信息：
  
  General Program of Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX0404);Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN202401624)
- DOI：10.12263/DZXB.20250110
  CLC： TP391;
- Received：13 February 2025，
  
  Revised：2025-06-03，
  
  Published：25 June 2025
- 稿件说明：
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赵宇, 舒巧媛. 基于渐进式混合对比学习的无监督领域自适应行人再识别[J]. 电子学报, 2025, 53(06): 1829-1846.

ZHAO Yu, SHU Qiao-yuan. Unsupervised Domain Adaptive Person Re-Identification Based on Progressive Hybrid Contrastive Learning[J]. Acta Electronica Sinica, 2025, 53(06): 1829-1846.
赵宇, 舒巧媛. 基于渐进式混合对比学习的无监督领域自适应行人再识别[J]. 电子学报, 2025, 53(06): 1829-1846. DOI：10.12263/DZXB.20250110

ZHAO Yu, SHU Qiao-yuan. Unsupervised Domain Adaptive Person Re-Identification Based on Progressive Hybrid Contrastive Learning[J]. Acta Electronica Sinica, 2025, 53(06): 1829-1846. DOI：10.12263/DZXB.20250110

摘要

无监督领域自适应（Unsupervised Domain Adaptation，UDA）行人再识别（person Re-IDentification，Re-ID）旨在利用有标注的源域数据来解决无标注目标域数据的无监督Re-ID任务.近期，对比学习在该领域引起关注，但现有方法存在正样本对差异较小以及忽略负代理采样偏差的问题.为解决这些问题，本文提出一种渐进式混合对比学习（Progressive Hybrid Contrastive Learning，PHCL）方法.在每个训练轮次，PHCL方法通过聚类和渐进细化两个步骤，将无标签数据集划分为带伪标签的聚类样本和未聚类的独立实例.基于聚类划分结果，PHCL方法在两个层次实施对比学习：通过将同一聚类（目标域）或同一身份标签（源域）中的相似样本拉近，指导模型学习类内相似性，同时通过在未聚类的实例间施加排斥作用，挖掘实例间差异性.此外，PHCL方法通过最近邻挖掘为未聚类的实例生成正代理，增大正样本对的差异性，学习更丰富的语义信息.同时，PHCL方法在负代理采样过程中去偏差，减轻假负代理对训练的不利影响.实验结果表明：PHCL方法在Market-1501和MSMT17数据集上的平均精度均值（mean Average Precision，mAP）分别为85.9%与42.3%，比基线模型分别提高4.3个百分点和13.5个百分点.上述实验结果验证了PHCL方法在UDA Re-ID任务中的有效性.

Abstract

Unsupervised domain adaptive (UDA) person re-identification (Re-ID) seeks to leverage labeled source domain data to address the task of unsupervised Re-ID in unlabeled target domain data. Recently

contrastive learning has attracted attention in this field. However

current methods suffer from small differences in positive sample pairs and overlook biases in negative proxy sampling. To resolve these challenges

this paper presents a progressive hybrid contrastive learning (PHCL) method. In each training epoch

the PHCL method divides the unlabeled dataset into clustered samples with pseudo-labels and un-clustered independent instances through two steps: clustering and progressive refinement. Based on the clustering results

PHCL implements contrastive learning at two different levels: to learn intra-category similarity through bringing together similar samples within the same cluster (target domain) or identity label (source domain) and explores inter-instance discrimination by applying repulsion among un-clustered individual instances. Moreover

the PHCL method generates positive proxies for anchor samples through nearest neighbor mining

increasing the differences among positive sample pairs to learn richer semantic information. Additionally

the PHCL method performs debiasing in the negative proxy sampling process

mitigating the adverse impact of false negative proxies on model training. Experimental results show that the PHCL method achieves mean average precision (mAP) of 85.9% and 42.3% on the Market-1501 and MSMT17 datasets

respectively

which are improvements of 4.3 percentage points and 13.5 percentage points over the baseline model. These results validate the efficacy of the PHCL method for UDA Re-ID.

关键词

Keywords

references

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