WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification
<|reference_start|>WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared Person Re-Identification: For the visible-infrared person re-identification (VI-ReID) task, one of the primary challenges lies in significant cross-modality discrepancy. Existing methods struggle to conduct modality-invariant information mining. They often focus solely on mining singular dimensions like spatial or channel, and overlook the extraction of specific-modality multi-dimension information. To fully mine modality-invariant information across a wide range, we introduce the Wide-Ranging Information Mining Network (WRIM-Net), which mainly comprises a Multi-dimension Interactive Information Mining (MIIM) module and an Auxiliary-Information-based Contrastive Learning (AICL) approach. Empowered by the proposed Global Region Interaction (GRI), MIIM comprehensively mines non-local spatial and channel information through intra-dimension interaction. Moreover, Thanks to the low computational complexity design, separate MIIM can be positioned in shallow layers, enabling the network to better mine specific-modality multi-dimension information. AICL, by introducing the novel Cross-Modality Key-Instance Contrastive (CMKIC) loss, effectively guides the network in extracting modality-invariant information. We conduct extensive experiments not only on the well-known SYSU-MM01 and RegDB datasets but also on the latest large-scale cross-modality LLCM dataset. The results demonstrate WRIM-Net's superiority over state-of-the-art methods.<|reference_end|>
@article{wu2024wrim-net:,
    title={WRIM-Net: Wide-Ranging Information Mining Network for Visible-Infrared
  Person Re-Identification},
    author={Yonggan Wu, Ling-Chao Meng, Yuan Zichao, Sixian Chan, Hong-Qiang Wang},
    journal={arXiv preprint arXiv:2408.10624},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10624},
    primaryClass={cs.CV cs.AI}
}