苏州科技大学学报（自然科学版）2026，Vol.43Issue(2)：62-69,8.DOI:10.12084/j.issn.2096-3289.2026.02.008
基于多语义特征互助学习的无监督跨模态行人重识别
Unsupervised cross-modal person re-identification based on multi-semantic feature mutual learning
摘要
Abstract
To address the issue of insufficient handling of feature differences in existing methods for unsupervised cross-modal person re-identification,this paper proposes an unsupervised cross-modal person re-identification method incorporating cluster consistency correction and multi-semantic feature fusion.By removing noisy samples and incorporating common samples,the method ensures that the mixed clusters of samples are more consistent with the characteristics of shallow and deep semantic feature clusters,thereby improving the homogeneity and complete-ness of cluster division.A multi-semantic feature exchange mechanism was introduced to achieve feature alignment and information interaction,enhancing the complementarity between features and reducing modality differences,thus alleviating overfitting.Experimental results demonstrate that the proposed method performs excellently on the SYSU-MM01 and RegDB datasets,significantly improving key metrics and validating its effectiveness and robustness in un-supervised cross-modal person re-identification tasks.关键词
无监督学习/行人重识别/多语义特征/特征交换Key words
unsupervised learning/person re-identification/multi-semantic features/feature exchange分类
信息技术与安全科学引用本文复制引用
陈峰,李果,杨甫中,屈喜文..基于多语义特征互助学习的无监督跨模态行人重识别[J].苏州科技大学学报（自然科学版）,2026,43(2):62-69,8.基金项目
国家自然科学基金项目(62206006) （62206006）
安徽省高校自然科学研究项目(2024AH040028) （2024AH040028）
