  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.
