Interaction and Alignment for Visible-Infrared Person Re-Identification

Visible-Infrared Person Re-Identification (VI-ReID) is a challenging person matching problem and is also a practical solution for intelligent surveillance systems at night. Due to the heterogeneity between visible and infrared modalities, the retrieval performance is seriously damaged. To address the issue of the discrepancy of the information between visible and infrared modalities, many works have been proposed. However, the relationship between cross-modality samples has rarely been mined. In this paper, we propose a Cross-modality Interaction and Alignment (CIA) module to solve the discrepancy problem. Through transforming the information between different modalities, the module guides the network to capture the modality-shared feature, which is beneficial to address the cross-modality discrepancy. Meanwhile, to better supervise the network, an enhanced contrastive loss is introduced. Contributed by the further optimization in the distance between intra-class samples, the network gains more effective supervision. Extensive experiments on two benchmark datasets show that our method achieves an excellent performance in VI-ReID.