Granular Semantic Cognition for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification aims to match pedes-trian images of the same identity captured by visible and infrared cam-eras, and is hindered by severe domain gaps due to the absence of colorin the infrared modality. Some methods employ text to supplement colorcues in infrared modality. However, they rely on global textual guidance,which hinders fine-grained alignment between textual color informationand local infrared semantics. To this end, this paper proposes a GranularSemantic Cognition (GSC) method, which leverages cross-modal sharedsemantics to facilitate the transfer of color features. Specifically, a Hierar-chical Refinement Module (HRM) progressively refines shared semanticprototypes via multi-stage interactions with pixel-wise features, yieldingfine-grained semantics. Based on these refined semantics, a Semantics-guided Adjustment Module (SAM) generates semantic-conditioned fu-sion weights to selectively aggregate textual color information in se-mantically relevant regions. In addition, a fusion-weighted identity con-trastive loss is proposed to alleviate semantic ambiguity across identi-ties by regularizing the semantics-guided fusion process. Extensive ex-perimental results conducted on the SYSU-MM01, RegDB and LLCMdatasets demonstrate the favorable performance.