Relational Inference with Specific-Shared Features for Visible-Infrared Person Re-Identification

Visible–infrared person re-identification (VI-ReID) aims to match pedestrians across heterogeneous visible and infrared modalities. Existing methods predominantly focus on learning modality-shared representations for cross-modality matching, which often suppress modality-specific discriminative cues. Some recent works attempt to introduce modality-specific features; however, their primary objective remains improving shared features for direct cross-modality matching, leaving the intrinsic potential of modality-specific information underexplored. In this paper, we propose a Specific–Shared Feature Inference (SSFI) framework that exploits modality-specific and modality-shared features in a fundamentally different manner. Instead of directly using modality-specific features for cross-modality matching, we leverage their strength in modeling intra-modality relationships. Specifically, a Specific–Shared Feature Extractor (SSFE) is designed to disentangle the two types of representations while enabling effective interaction between them. Furthermore, we introduce a Cross-Modality Similarity Inference (CSI) module, which utilizes modality-specific features to construct intra-modality affinity among gallery samples and propagates such relational information to refine cross-modality similarity estimation based on modality-shared features. Extensive experiments on standard VI-ReID benchmarks demonstrate that the proposed method consistently and significantly outperforms state-of-the-art approaches.