CMIA-Net: Early Cross-Modal Interaction for Visible-Infrared Person Re-Identification

Abstract

Visible-infrared person re-identification (VI-ReID) remains challenging due to severe spectral discrepancy and local cross-modal misalignment between visible and infrared images. Most existing methods alleviate this discrepancy through middle- or late-stage feature alignment, but modality-specific shallow features may have already accumulated spectral bias and local correspondence errors before shared representations are formed. In this paper, we argue that VI-ReID should be treated as an early cross-modal correspondence learning problem rather than only a late embedding alignment problem. To this end, we propose \textbf{CMIA-Net}, a framework that establishes bidirectional visible-infrared interaction at shallow backbone stages. Its core module, \textbf{Cross-Modal Interaction Attention (CMIA)}, enables visible and infrared feature maps to exchange complementary local information before deep semantic aggregation, thereby reducing progressive stream divergence. To stabilize early interaction, we further introduce \textbf{Spectral-Invariant Augmentation (MC-Aug)} to suppress over-reliance on visible-spectrum cues and a \textbf{Bi-directional Hetero-Center Learning (BHCT)} loss to improve class-center-level cross-modal compactness and inter-class separability. Experiments on RegDB and SYSU-MM01 show that CMIA-Net achieves strong performance on RegDB and the SYSU-MM01 indoor-search protocol, while remaining competitive under the more challenging SYSU-MM01 all-search setting. Specifically, CMIA-Net obtains 94.09\% Rank-1 accuracy and 90.74\% mAP in the RegDB visible-to-thermal setting, and 66.67\% Rank-1 accuracy and 58.12\% mAP in the SYSU-MM01 all-search setting.