Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification

• URL: http://arxiv.org/abs/2409.05642v1
• Date: Mon, 9 Sep 2024 14:12:23 GMT
• Title: Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification
• Authors: Jiarui Li, Zhen Qiu, Yilin Yang, Yuqi Li, Zeyu Dong, Chuanguang Yang,
• Abstract summary: Primary challenges in visible-infrared person re-identification arise from the differences between visible (vis) and infrared (ir) images. Existing methods often rely on horizontal partitioning to align part-level features, which can introduce inaccuracies. We propose a novel Prototype-Driven Multi-feature generation framework (PDM) aimed at mitigating cross-modal discrepancies.
• Score: 11.664820595258988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The primary challenges in visible-infrared person re-identification arise from the differences between visible (vis) and infrared (ir) images, including inter-modal and intra-modal variations. These challenges are further complicated by varying viewpoints and irregular movements. Existing methods often rely on horizontal partitioning to align part-level features, which can introduce inaccuracies and have limited effectiveness in reducing modality discrepancies. In this paper, we propose a novel Prototype-Driven Multi-feature generation framework (PDM) aimed at mitigating cross-modal discrepancies by constructing diversified features and mining latent semantically similar features for modal alignment. PDM comprises two key components: Multi-Feature Generation Module (MFGM) and Prototype Learning Module (PLM). The MFGM generates diversity features closely distributed from modality-shared features to represent pedestrians. Additionally, the PLM utilizes learnable prototypes to excavate latent semantic similarities among local features between visible and infrared modalities, thereby facilitating cross-modal instance-level alignment. We introduce the cosine heterogeneity loss to enhance prototype diversity for extracting rich local features. Extensive experiments conducted on the SYSU-MM01 and LLCM datasets demonstrate that our approach achieves state-of-the-art performance. Our codes are available at https://github.com/mmunhappy/ICASSP2025-PDM.

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