Identity Clue Refinement and Enhancement for Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2512.04522v1
• Date: Thu, 04 Dec 2025 07:13:38 GMT
• Title: Identity Clue Refinement and Enhancement for Visible-Infrared Person Re-Identification
• Authors: Guoqing Zhang, Zhun Wang, Hairui Wang, Zhonglin Ye, Yuhui Zheng,
• Abstract summary: Visible-Infrared Person Re-Identification (VI-ReID) is a challenging cross-modal matching task due to significant modality discrepancies.<n>We propose a novel Identity Clue Refinement and Enhancement (ICRE) network to mine and utilize the implicit discriminative knowledge inherent in modality-specific attributes.
• Score: 20.544872117860915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible-Infrared Person Re-Identification (VI-ReID) is a challenging cross-modal matching task due to significant modality discrepancies. While current methods mainly focus on learning modality-invariant features through unified embedding spaces, they often focus solely on the common discriminative semantics across modalities while disregarding the critical role of modality-specific identity-aware knowledge in discriminative feature learning. To bridge this gap, we propose a novel Identity Clue Refinement and Enhancement (ICRE) network to mine and utilize the implicit discriminative knowledge inherent in modality-specific attributes. Initially, we design a Multi-Perception Feature Refinement (MPFR) module that aggregates shallow features from shared branches, aiming to capture modality-specific attributes that are easily overlooked. Then, we propose a Semantic Distillation Cascade Enhancement (SDCE) module, which distills identity-aware knowledge from the aggregated shallow features and guide the learning of modality-invariant features. Finally, an Identity Clues Guided (ICG) Loss is proposed to alleviate the modality discrepancies within the enhanced features and promote the learning of a diverse representation space. Extensive experiments across multiple public datasets clearly show that our proposed ICRE outperforms existing SOTA methods.

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