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 multigranularity semantic information related to pedestrian body structure attributes.However, existing researchmainly 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 DeepMulti-Granularity Pose-Aware Feature FusionNetwork (ADMPFF-Net), incorporating theMulti-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. [ABSTRACT FROM AUTHOR]