Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network for Visible-Infrared Person Re-Identification

Visible-infrared Cross-modality Person Re-identification (VI-ReID) is a critical technology in smart public facilities such as cities, campuses and libraries. It aims to match pedestrians in visible light and infrared images for video surveillance, which poses a challenge in exploring cross-modal shared information accurately and efficiently. Therefore, multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes. However, existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks, the fusion module. This paper introduces a novel network called the Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network (ADMPFF-Net), incorporating the Multi-Granularity Pose-Aware Feature Fusion (MPFF) module to generate discriminative representations. MPFF efficiently explores and learns global and local features with multi-level semantic information by inserting disentangling and duplicating blocks into the fusion module of the backbone network. ADMPFF-Net also provides a new perspective for designing multi-granularity learning networks. By incorporating the multi-granularity feature disentanglement (mGFD) and posture information segmentation (pIS) strategies, it extracts more representative features concerning body structure information. The Local Information Enhancement (LIE) module augments high-performance features in VI-ReID, and the multi-granularity joint loss supervises model training for objective feature learning. Experimental results on two public datasets show that ADMPFF-Net efficiently constructs pedestrian feature representations and enhances the accuracy of VI-ReID.