MGFNet: A Multi-granularity Feature Fusion and Mining Network for Visible-Infrared Person Re-identification

Visible-infrared person re-identification (VI-ReID) aims to match the same pedestrian in different forms captured by the visible and infrared cameras. Existing works on retrieving pedestrians focus on mining the shared feature representations by the deep convolutional neural networks. However, there are limitations of single-granularity for identifying target pedestrians in complex VI-ReID tasks. In this study, we propose a new Multi-Granularity Feature Fusion and Mining Network (MGFNet) to fuse and mine the feature map information of the network. The network includes a Local Residual Spatial Attention (LRSA) module and a Multi-Granularity Feature Fusion and Mining (MGFM) module to jointly extract discriminative features. The LRSA module aims to guide the network to learn fine-grained features that are useful for discriminating and generating more robust feature maps. Then, the MGFM module is employed to extract and fuse pedestrian features at both global and local levels. Specifically, a new local feature fusion strategy is designed for the MGFM module to identify subtle differences between various pedestrian images. Extensive experiments on two mainstream datasets, SYSU-MM01 and RegDB, show that the MGFNet outperforms the existing techniques.