Multi-granularity Feature Utilization Network for Cross-modality Visible-Infrared Person Re-identification

Abstract Cross-modality Visible-Infrared Person Re-identification (VI-ReID) aims to recognize images with the same identity between visible modality and infrared modality, which is a very challenging task. Because it not only includes the trouble of variations between cross-cameras in traditional person re-identification, but also suffers from the huge differences between two modalities. Some existing VI-ReID methods are often limited to single learning of global features or local features, while ignoring the complementarity between fine-grained and coarse-grained information. To solve this problem, our paper designs a multi-granularity feature utilization network (MFUN), which makes up for the lack of modality-shared information by learning fine-grained and coarse-grained features. Firstly, for the sake of learning fine-grained information, a local feature constraint module is introduced, in this module we use hard-mining triplet loss and heterogeneous center loss to constrain local features simultaneously to better promote intra-class closeness and inter-class differences at the coarse and fine granularity levels. Then, our method uses a multi-modality feature aggregation module for global features to fuse the information of two modalities to narrow the modality gap. Through the combination of these two modules, visible and infrared image features can be better fused, thus alleviating the problem of modality discrepancy and supplementing the lack of modality-shared information. Extensive experimental results on RegDB and SYSU-MM01 datasets fully prove that our proposed MFUN has superiority over the state-of-the-art solutions.