Implicit Discriminative Knowledge Learning for Visible-Infrared Person Re-Identification
<|reference_start|>Implicit Discriminative Knowledge Learning for Visible-Infrared Person Re-Identification: Visible-Infrared Person Re-identification (VI-ReID) is a challenging cross-modal pedestrian retrieval task, due to significant intra-class variations and cross-modal discrepancies among different cameras. Existing works mainly focus on embedding images of different modalities into a unified space to mine modality-shared features. They only seek distinctive information within these shared features, while ignoring the identity-aware useful information that is implicit in the modality-specific features. To address this issue, we propose a novel Implicit Discriminative Knowledge Learning (IDKL) network to uncover and leverage the implicit discriminative information contained within the modality-specific. First, we extract modality-specific and modality-shared features using a novel dual-stream network. Then, the modality-specific features undergo purification to reduce their modality style discrepancies while preserving identity-aware discriminative knowledge. Subsequently, this kind of implicit knowledge is distilled into the modality-shared feature to enhance its distinctiveness. Finally, an alignment loss is proposed to minimize modality discrepancy on enhanced modality-shared features. Extensive experiments on multiple public datasets demonstrate the superiority of IDKL network over the state-of-the-art methods. Code is available at https://github.com/1KK077/IDKL.<|reference_end|>
@article{ren2024implicit,
    title={Implicit Discriminative Knowledge Learning for Visible-Infrared Person
  Re-Identification},
    author={Kaijie Ren and Lei Zhang},
    journal={arXiv preprint arXiv:2403.11708},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11708},
    primaryClass={cs.CV}
}