Cross-Modality Self-Distillation for Visible-Infrared Person Re-Identification

Visible-Infrared Person Re-identification (VI-ReID) faces large challenges due to the cross-modality gap and intra-class variations. Existing methods mainly focus on mitigating the cross-modality discrepancy by optimizing the embedding space of high-level features. However, two critical issues are often overlooked: low-level features suffer from larger modality gaps, and redundant channels exhibit limited identity discrimination. These limitations hinder the overall discriminative performance of the model. To address the above issues, we propose a novel Cross-Modality Self-Distillation (CMSD) network, which consists of two key components: Cross-layer Semantic Distillation (CSD) and Intra-layer Alignment Distillation (IAD). These two modules are mutually beneficial to jointly explore cross-layer modality discrepancy and intra-layer channel discrepancy. Specifically, CSD distills modality-shared semantics from high-level features to guide low-level features in learning more reliable modality-invariant representations. Furthermore, IAD decomposes feature maps into salient and redundant channels while transferring identity-related information from salient to redundant channels, thereby enhancing overall discrimination of modality-invariant features. Our extensive experiments on the SYSU-MM01, RegDB, and LLCM datasets demonstrate that the components of our proposed CMSD mutually enhance each other, leading to superior performance compared to state-of-the-art methods.