DBFormer: Dual Branch Transformer for Visible-Infrared Person Re-identification

Conventional visible-light-based person re-identification (Person Re-ID) techniques suffer performance degradation in nighttime or low-light conditions, while visible-infrared (RGB-IR) person Re-ID can adapt to multiple indoor and nocturnal scenarios. However, the latter faces dual challenges: significant feature distribution discrepancies and local-global feature representation imbalance. Recently, Transformer architectures and part-based methods have demonstrated great progress in traditional Re-ID tasks; however, their direct application to cross-modal scenarios exhibits critical limitations, such as compromised feature integrity from excessive fine-grained segmentation and substantially increased computational complexity. To address these challenges, we propose a Dual-Branch Transformer network (DBFormer) which horizontally partitions the feature encoding process into upper-body and lower-body branches, thereby enhancing the detailed feature modeling capability. Moreover, we design a dual-branch alignment loss function to enforce feature distribution consistency and mitigate inter-branch discrepancies, and a cross-modal alignment loss function to significantly improve Re-ID performance by optimizing cross-modal feature distances. Extensive experiments demonstrate that our method achieves superior accuracy in person re-identification, outperforming state-of-the-art approaches in recent years.