A multi-stage feature alignment framework with cross-modality collaborative fusion for visible-infrared person re-identification

Abstract

Abstract Efficient implementation of Visible-Infrared Person Re-identification (VI-ReID) is important for intelligent transportation and surveillance systems, where the primary challenge lies in the semantic discrepancy between visible and infrared modalities. Although existing methods have made progress in mitigating modality discrepancies, single-stage feature alignment remains prone to semantic shifts. We argue that hierarchical transitions in the feature alignment process can alleviate this issue. Based on this observation, we propose a Multi-Stage Feature Alignment and Cross-Modality Collaborative Fusion (MS-CF) framework, which performs multi-dimensional modality discrepancy optimization and suppresses redundant information, thereby ensuring cross-modality consistency and intra-modality stability. The MS-CF framework consists of three modules. The Dual-Path Cross-Layer Attention (DCA) module enhances semantic and structural representations of intermediate features. The Balanced Feature Normalization (BFN) module improves feature distribution consistency and discriminability by incorporating modality constraints and feature sparsification. The Multi-Stage Hybrid-Modality Alignment (MS-HMA) strategy applies collaborative constraints to hybrid modalities after initial single-modality alignment, enabling coarse-to-fine semantic convergence. Extensive experiments on the SYSU-MM01 and RegDB datasets demonstrate that the MS-CF framework outperforms state-of-the-art methods. In particular, under the indoor-search setting of SYSU-MM01, MS-CF achieves improvements of 5.75% in Rank-1 accuracy and 3.97% in mAP, validating its effectiveness in enhancing robustness and recognition performance.