Person re-identification via pose-aware multi-semantic learning

• Main Authors: Luo, Xiangzhong, Duong, Luan H. K., Liu, Weichen
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2023
• Subjects: Engineering::Computer science and engineering; Person Re-Identification; Multi-Level Semantics
• Online Access: https://hdl.handle.net/10356/165561
• Institution: Nanyang Technological University

Person re-identification (ReID) remains an open-ended research topic, with its variety of substantial applications such as tracking, searching, etc. Existing methods mostly explore the highest-semantic feature embedding, ignoring the insights hidden among the earlier layers. Moreover, owing to the misalignment and pose variations, pose-related information is of great significance and needs to be comprehensively utilized. In this paper, we present a novel person ReID framework called Pose-aware Multi-semantic Fusion Network (PMFN). First, taking into account multiple semantics, we propose Multi-semantic Fusion Network (MFN) as the backbone, employing several shortcuts to reserve bypass feature maps for subsequent fusion. Second, to learn a pose-sensitive embedding, pose-aware clues are considered, forming the complete PMFN and investigating the well-aligned global and local body regions. Finally, the center loss is introduced for enhancing the feature discriminability. Exhaustive experiments on two large-scale person ReID benchmarks demonstrate the strengths of our approach over recent state-of-the-art works.