陕西师范大学学报（自然科学版）2026，Vol.54Issue(5)：65-80,16.DOI:10.15983/j.cnki.jsnu.2026507
基于动态特征融合与特征感知的可见光-红外行人重识别方法
Visible-infrared person re-identification via dynamic feature fusion and feature-awareness
摘要
Abstract
Visible-infrared person re-identification(VI-ReID)remains challenging due to cross-modal discrepancies and intra-class variations.While existing methods focus on feature space alignment for cross-modal shared representations,they often neglect modality-specific discriminative features such as visible textures and infrared thermal patterns.To address this limitation,a visible-infrared person re-identification framework via dynamic feature fusion and feature-awareness(DFFA)is proposed.Leveraging Transformer's long-range modeling capabilities,DFFA dynamically integrates local visible textures with global infrared thermal features through cross-modal attention,effectively preserving complementary modality-specific and shared characteristics.Our framework features a multi-dimensional loss design:a modality-fusion contrastive loss aligns local visible features,global infrared features,and auxiliary representations,an intra-modality consistency loss stabilizes feature learning,a global classification loss enhances identity discrimination.Additionally,a cross-modal optimized k-reciprocal re-ranking algorithm to mitigate modality bias in neighborhood similarity assessment is developed.Extensive experiments on three datasets(SYSU-MM01,RegDB,and LLCM)demonstrate state-of-the-art performance:achieving Rank-1 accuracies of 76.35％(SYSU-MM01,All-search)and 92.83％(RegDB,VIS to IR)with mAP scores of 79.56％and 88.68％,respectively.In complex low-light scenarios of LLCM,Rank-1 reaches 68.2％(VIS to IR)and 56.5％(IR to VIS)with mAP of 66.3％and 63.4％.These results validate the synergistic effectiveness of our dynamic fusion strategy,multi-loss design,and enhanced re-ranking approach.关键词
行人重识别/跨模态/特征融合/对比损失函数/k-互近邻重排序算法Key words
person re-identification/cross-modality/feature fusion/contrastive loss/k-reciprocal re-ranking algorithm分类
信息技术与安全科学引用本文复制引用
史娜,陈琳琳,何强,张长伦..基于动态特征融合与特征感知的可见光-红外行人重识别方法[J].陕西师范大学学报（自然科学版）,2026,54(5):65-80,16.基金项目
国家自然科学基金(12301581) （12301581）
北京市自然科学基金(4252033) （4252033）
北京建筑大学基本科研业务费(X25039) （X25039）
