Identity-Compensated Style Distillation for Visible-Infrared Person Re-Identification

Visible-Infrared Person Re-Identification (VI-ReID) that matches pedestrian images across visible and infrared modalities suffers from substantial modality discrepancies and intra-class variations. While existing methods typically address the modality gap via style alignment, they often lose identity-relevant semantics and overlook fine-grained inter-class nuances, such as body part contours and structural cues around the head, shoulders, or feet. To tackle these challenges, we propose an Identity-Compensated Style Distillation (ICSD) network that enforces cross-modality style consistency and enhances the discriminative power of modality-invariant features. Specifically, ICSD comprises two core components: (1) a Style Knowledge Distillation (SKD) module, which integrates Style Discrepancy Reduction (SDR) and Identity Knowledge Compensation (IKC) to align modality styles while preserving identity-relevant semantics; (2) an Identity Discrimination Amplification (IDA) module, which captures and enhances subtle inter-class differences by refining identity-specific cues, thereby facilitating more accurate discrimination between different pedestrians. Extensive experiments on three public benchmarks-SYSU-MM01, RegDB, and LLCM-demonstrate that ICSD consistently outperforms state-of-the-art methods, validating the effectiveness and complementarity of its components.