Bi-Level Inter-Modality Modulation for Unsupervised Visible-Infrared Person Re-Identification

The task of unsupervised visible–infrared person re-identification (USL-VI-ReID) aims to retrieve cross-modal pedestrian images without manual annotations. The key challenge lies in achieving semantic alignment to resolve modality bias in the absence of real labels. However, existing methods overly rely on single-modal information in the process of pseudo-label generation without considering cross-modal associations, making it difficult to bridge the modality gap between visible and infrared images. To address these issues, this paper proposes a Bi-level Inter-Modal Modulation Network (BIMM-Net), which employs multi-level cluster structure optimization as a core strategy to drive the establishment of…

• NN
  National Natural Science Foundation of China

  Awards: 62501226, 62576067
• NS
  Natural Science Foundation of Hebei Province

  Award: F2025201037
• NS
  Natural Science Foundation of Liaoning Province

  Awards: 2025-YQ-01, 2024-MS-012
• DS
  Dalian Science and Technology Innovation Fund

  Award: 2024RY010