Hierarchical Token-Aware Cross-Modality Reconstruction for Visible-Infrared Person Re-Identification

作者：Si Chen, Liuxiang Qiu, Da‐Han Wang, Wentao Zhu, Hua Yang, Yan Yan · 发表于：IEEE Transactions on Multimedia · 年份：2025 · DOI：10.1109/tmm.2025.3590933 · 被引用次数：9 · 研究领域：Video Surveillance and Tracking Methods、Gait Recognition and Analysis、Advanced Neural Network Applications

Visible-infrared person re-identification (VI-ReID) aims to query the same pedestrian's visible (infrared) images in the gallery set from the infrared (visible) images. VI-ReID not only needs to deal with the challenging factors like pose variation and occlusion, but also requires handling the large modality discrepancy. Previous methods mainly focus on learning single-scale modality-shared features and do not effectively explore the multi-scale features of two modalities from both short-range and long-range perspectives. In order to solve these problems, this paper proposes a novel Hierarchical Token-Aware Cross-Modality Reconstruction (HTCR) network to significantly mitigate the modality discrepancy for effective VI-ReID. The HTCR network consists of two main components, i.e., Hierarchical Token-aware Fusion (HTF) and Cross-modality Feature Reconstruction (CFR). The HTF module first bidirectionally exchanges the short-range and long-range multi-scale modality-shared features with a few learnable tokens to achieve discriminative pedestrian features by making full use of the advantages of both Convolutional Neural Network (CNN) and Transformer. Moreover, the CFR module reconstructs global and local pedestrian features of one modality by using the token sequence of the other modality with multi-scale cues to further explore the relationship between the two distinct modalities and alleviate the modality discrepancy. In addition, the Modality-shared feature Reconstruction (MR) l...