Mixed Modality Generation and Hierarchical Feature Aggregation for Visible‐Infrared Person Re‐Identification

ABSTRACT The main challenge in visible‐infrared person re‐identification (VI‐ReID), which involves matching images of target persons across different modalities, is the significant modality gap between visible and infrared images. Current approaches generally fall into two categories: network architectures that map features from different modalities into a shared feature space, and methods that focus on modality generation and transformation. However, these methods often fail to fully account for contextual relationships, and the generated modalities may lack semantic interpretability. To address these limitations, this paper proposes a mixed modality generator that aligns the visible and infrared modalities as closely as possible within a unified feature space. To effectively leverage multi‐layered information, we introduce a hierarchical feature aggregation module, which establishes connections between features across different layers. Comprehensive experiments on the SYSU‐MM01, RegDB, and LLCM datasets demonstrate that our method significantly outperforms existing state‐of‐the‐art approaches, setting a new benchmark for VI‐ReID performance.