Visible-Infrared Person Re-Identification

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13 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Semantic Modality Compensation for Unsupervised Visible-Infrared Person Re-identification under Unpaired Settings
Sep 28, 2026Duanning Chen, Ke He, Bin Yang +1Missing-Modality Learning Multimodal Disentangled Representation Learning
Wuhan University
Prototype Matters: Modality-unified Prototype Self-distillation for Unsupervised Visible-infrared Person Re-identification
Sep 10, 2026Menglin Wang, Xiaojin GongContrastive Learning Cross-Modal Alignment
Nanjing Normal University Nanjing, China · Zhejiang University Hangzhou, China
Multi-scale Decomposed Convolution Refinement Network for Visible-Infrared Person Re-Identification
Aug 17, 2026Mingsheng Zheng, Zirui Jiang, Bo Liu +3Visible-Infrared Person Re-Identification Person Re-Identification
School of Computer Science and Technology, Xinjiang University, Urumqi, Xinjiang 830046, China · Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing, Xinjiang University, Urumqi, Xinjiang 830046, China
Dual-Edged Homogeneous-Modality Similarity: Towards Visible-Infrared Modality-Incomplete Person Re-Identification with Modality Adaptive Matching
Jul 21, 2026Xin Xu, Shuhao Zhan, Wei Liu +3Missing-Modality Learning Cross-Modal Representation Learning
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, Hubei, China. · Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China. · School of Computer Science, Wuhan University, Wuhan, 430079, Hubei, China. +2
Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification
Jul 16, 2026Xiao Wang, Bing Wang, Bin Yang +3Multimodal Fusion Visible-Infrared Person Re-Identification
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, 430065, China. · Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan University of Science and Technology, Wuhan, Hubei, 430065, China. · School of Computer, Wuhan University, Wuhan, Hubei, 430072, China. +1
MFEN:Multi-Frequency Expert Network for Visible-Infrared Person Re-ID
Jun 10, 2026Xulin Li, Yan Lu, Bin Liu +4Frequency-Domain Feature Learning Visible-Infrared Person Re-Identification
University of Science and Technology of China, China · Anhui Province Key Laboratory of Digital Security, China · The Chinese University of Hong Kong, China
Temporal Prototyping and Hierarchical Alignment for Unsupervised Video-based Visible-Infrared Person Re-Identification
Apr 23, 2026Zhiyong Li, Wei Jiang, Haojie Liu +3Prototype Learning Video Representation Learning
College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China · School of Computer Science and Technology, Zhejiang University of Water Resources and Electric Power, Hangzhou, 310018, China
Causal Bootstrapped Alignment for Unsupervised Video-Based Visible-Infrared Person Re-Identification
Apr 17, 2026Shuang Li, Jiaxu Leng, Changjiang Kuang +3Cross-Modal Alignment Unsupervised Representation Learning
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
CMCC-ReID: Cross-Modality Clothing-Change Person Re-Identification
Apr 3, 2026Haoxuan Xu, Hanzi Wang, Guanglin NiuVisible-Infrared Person Re-Identification Person Re-Identification
School of Artificial Intelligence, Beihang University, Beijing, China · School of Informatics, Xiamen University, Xiamen, Fujian, China
Homogeneous and Heterogeneous Consistency progressive Re-ranking for Visible-Infrared Person Re-identification
Mar 17, 2026Yiming WangVisible-Infrared Person Re-Identification Person Re-Identification
BIT: Matching-based Bi-directional Interaction Transformation Network for Visible-Infrared Person Re-Identification
Mar 15, 2026Haoxuan Xu, Guanglin NiuVisible-Infrared Person Re-Identification Person Re-Identification
School of Artificial Intelligence, Beihang University
Learning Language-Driven Sequence-Level Modal-Invariant Representations for Video-Based Visible-Infrared Person Re-Identification
Jan 17, 2026Xiaomei Yang, Antai Liu, Xizhan Gao +3Invariant Representation Learning Visible-Infrared Person Re-Identification
Shandong Key Laboratory of Ubiquitous Intelligent Computing, School of Information Science and Engineering, University of Jinan, Jinan 250022, China · College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China · Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, Via P. Bucci, cubo 41C, 87036, Rende (CS), Italy
From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification
Mar 15, 2025Yan Jiang, Hao Yu, Xu Cheng +3Privacy-Preserving ML Visible-Infrared Person Re-Identification
Center for Machine Vision and Signal Analysis, University of Oulu, Oulu FI-90014, Finland · School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China

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Semantic Modality Compensation for Unsupervised Visible-Infrared Person Re-identification under Unpaired Settings

Duanning Chen, Ke He, Bin Yang, Yongxiang Yao

Wuhan University

Missing-Modality Learning Multimodal Disentangled Representation Learning Multimodal Learning Person Re-Identification Visible-Infrared Person Re-Identification

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Unsupervised visible-infrared person re-identification (USL-VI-ReID) learns person representations that can be compared across modalities without identity annotations. In the unpaired setting, however, identity correspondences between modalities are often incomplete, leaving many identities without an observed counterpart in the other modality. Existing unpaired methods bridge this gap by generating or mapping features for the other modality, mainly by exploiting the statistics of visual features without explicitly separating content that is discriminative for identity from style that is specific to modality. Consequently, the generated features may distort identity cues or inherit bias from the source modality, undermining the reliability of supervision across modalities. We formulate unpaired learning across modalities as a semantic compensation problem and propose Semantic Modality Compensation (SMC), a framework based on prompt composition that decouples identity semantics from modality style within a shared visual semantic space. SMC first constructs a discriminative ReID space through augmented dual contrastive learning, yielding pseudo labels, cluster prototypes, and memory banks for each modality. It then learns visible and infrared modality prompts in the CLIP semantic space and maps clusters obtained from pseudo labels to identity semantic tokens. For each cluster lacking a reliable match in the other modality, SMC combines its identity token with the prompt for the target modality to synthesize a semantic counterpart in the missing modality. The synthesized counterpart is then projected back into the ReID space and injected into a compensation memory through confidence gating. Extensive experiments under both paired and unpaired settings demonstrate that SMC consistently outperforms state-of-the-art methods, with particularly large gains when identity mismatch is severe.