High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-Identification

• URL: http://arxiv.org/abs/2312.07853v2
• Date: Thu, 14 Dec 2023 02:05:03 GMT
• Title: High-Order Structure Based Middle-Feature Learning for Visible-Infrared Person Re-Identification
• Authors: Liuxiang Qiu, Si Chen, Yan Yan, Jing-Hao Xue, Da-Han Wang, Shunzhi Zhu
• Abstract summary: Visible-infrared person re-identification (VI-ReID) aims to retrieve images of the same persons captured by visible (VIS) and infrared (IR) cameras. Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space. We propose a novel high-order structure based middle-feature learning network (HOS-Net) for effective VI-ReID.
• Score: 37.954344873390106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible-infrared person re-identification (VI-ReID) aims to retrieve images of the same persons captured by visible (VIS) and infrared (IR) cameras. Existing VI-ReID methods ignore high-order structure information of features while being relatively difficult to learn a reasonable common feature space due to the large modality discrepancy between VIS and IR images. To address the above problems, we propose a novel high-order structure based middle-feature learning network (HOS-Net) for effective VI-ReID. Specifically, we first leverage a short- and long-range feature extraction (SLE) module to effectively exploit both short-range and long-range features. Then, we propose a high-order structure learning (HSL) module to successfully model the high-order relationship across different local features of each person image based on a whitened hypergraph network.This greatly alleviates model collapse and enhances feature representations. Finally, we develop a common feature space learning (CFL) module to learn a discriminative and reasonable common feature space based on middle features generated by aligning features from different modalities and ranges. In particular, a modality-range identity-center contrastive (MRIC) loss is proposed to reduce the distances between the VIS, IR, and middle features, smoothing the training process. Extensive experiments on the SYSU-MM01, RegDB, and LLCM datasets show that our HOS-Net achieves superior state-of-the-art performance. Our code is available at \url{https://github.com/Jaulaucoeng/HOS-Net}.

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