Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification

• URL: http://arxiv.org/abs/2312.07021v2
• Date: Tue, 19 Dec 2023 02:46:50 GMT
• Title: Transferring Modality-Aware Pedestrian Attentive Learning for Visible-Infrared Person Re-identification
• Authors: Yuwei Guo, Wenhao Zhang, Licheng Jiao, Shuang Wang, Shuo Wang, and Fang Liu
• Abstract summary: We propose a novel Transferring Modality-Aware Pedestrian Attentive Learning (TMPA) model. TMPA focuses on the pedestrian regions to efficiently compensate for missing modality-specific features. experiments conducted on the benchmark SYSU-MM01 and RegDB datasets demonstrated the effectiveness of our proposed TMPA model.
• Score: 43.05147831905626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visible-infrared person re-identification (VI-ReID) aims to search the same pedestrian of interest across visible and infrared modalities. Existing models mainly focus on compensating for modality-specific information to reduce modality variation. However, these methods often lead to a higher computational overhead and may introduce interfering information when generating the corresponding images or features. To address this issue, it is critical to leverage pedestrian-attentive features and learn modality-complete and -consistent representation. In this paper, a novel Transferring Modality-Aware Pedestrian Attentive Learning (TMPA) model is proposed, focusing on the pedestrian regions to efficiently compensate for missing modality-specific features. Specifically, we propose a region-based data augmentation module PedMix to enhance pedestrian region coherence by mixing the corresponding regions from different modalities. A lightweight hybrid compensation module, i.e., the Modality Feature Transfer (MFT), is devised to integrate cross attention and convolution networks to fully explore the discriminative modality-complete features with minimal computational overhead. Extensive experiments conducted on the benchmark SYSU-MM01 and RegDB datasets demonstrated the effectiveness of our proposed TMPA model.

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