Progressively Biased Split Vision Transformer Learning for Visible-Infrared Person Re-Identification

Abstract

Visible-Infrared Person Re-Identification (VI-ReID) remains a challenging task due to the significant modality gap between visible and infrared images, which hinders accurate cross-modality identity matching. Existing methods often struggle to balance modality invariance and feature discriminability. Most methods employ ImageNet-pretrained backbones that are heavily biased toward RGB statistics, causing the extracted cross-modal features to over-emphasize visible-spectrum information and weakening the understanding of infrared cues. To address this issue, some works introduce third-modality generation or grayscale-based augmentation, but these strategies either increase training complexity or still leave a non-negligible discrepancy from real infrared data. We propose a Progressively Biased Split Vision Transformer (PBSVT), which combines a split ViT backbone with progressive bias training to gradually reduce RGB-dominant bias while preserving modality-shared structure. Extensive experiments on SYSU-MM01 and RegDB show that PBSVT achieves state-of-the-art or highly competitive performance while introducing no additional inference cost. PBSVT obtains the best results on 8 of the 12 reported indicators, including 77.90% Rank-1 and 97.98% Rank-10 on SYSU-MM01 all-search, 83.74% Rank-1 on SYSU-MM01 indoor-search, and 92.89% Rank-1, 98.77% Rank-10, and 92.18% mAP on RegDB Visible-to-Infrared. These results demonstrate the effectiveness of progressive modality transition for robust VI-ReID representation learning.