SSRR: Structural Semantic Representation Reconstruction for Visible-Infrared Person Re-Identification

作者：Xi Yang, Menghui Tian, Meijie Li, Ziyu Wei, Liu Yuan, Nannan Wang, Xinbo Gao · 发表于：IEEE Transactions on Multimedia · 年份：2023 · DOI：10.1109/tmm.2023.3347855 · 被引用次数：25 · 研究领域：Video Surveillance and Tracking Methods、Advanced Neural Network Applications、Human Pose and Action Recognition

Visible-infrared Person Re-identification (VI-ReID) aims to retrieve the images of pedestrian with the same identity from different modalities and cameras given a pedestrian image. To reduce modality discrepancy, existing methods often perform hard partitioning to mine more detail. However, these methods employ only uniform partitioning, without considering pedestrian structure, and lose a lot of pedestrian semantic information. To this end, this paper proposes a structural semantic representation reconstruction (SSRR) method to capture pedestrian semantic information by focusing on pedestrian structure. Specifically, based on the fine-grained features obtained by hard partitioning, we carry out structural reconstruction to obtain the reconstructed features containing semantic information. By adopting the direct link reconstruction structure, the reciprocal learning of fine-grained features and semantic features is ensured. Semantic features are reconstructed based on fine-grained features, and semantic information is beneficial to fine-grained features to better capture pedestrian-related details. In addition, local consistency loss is introduced to ensure the consistency of fine-grained features in the same component location, further enhancing the discriminant of the learned reconstructed representation. Extensive experiments confirm the superiority of our method on two public datasets SYSU-MM01 and RegDB.