Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification
<|reference_start|>Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification: 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.<|reference_end|>
@article{li2024prototype-driven,
    title={Prototype-Driven Multi-Feature Generation for Visible-Infrared Person
  Re-identification},
    author={Jiarui Li, Zhen Qiu, Yilin Yang, Yuqi Li, Zeyu Dong, Chuanguang Yang},
    journal={arXiv preprint arXiv:2409.05642},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05642},
    primaryClass={cs.CV}
}