Learning Source-Free Domain Adaptation for Visible-Infrared Person Re-Identification
Yongxiang Li, Yanglin Feng, Yuan Sun, Dezhong Peng, Xi Peng, Peng Hu
In this paper, we investigate source-free domain adaptation (SFDA) for Visible-Infrared Person Re-Identification (VI-ReID), aiming to adapt a pre-trained source model to an unlabeled target domain without access to source domain data or target domain labels.  To tackle this, we propose a novel learning paradigm, Source-Free Domain Adaptation for Visible-Infrared Person Re-Identification (SVIP), which fully exploits the prior knowledge embedded in the source model to guide target domain adaptation.  Our method consists of three core mechanisms designed specifically for the source-free setting: (1) the Source Guided Contrastive Learning (SGCL) mechanism, which leverages the discriminative feature space of the fixed source model as a reference to perform contrastive learning on unlabeled target data, thereby preserving discriminative capability without requiring source samples;  (2) the Residual Transfer Learning (RTL) mechanism, which learns a residual mapping to adapt the target model’s representations towards the target domain while maintaining the source model’s prior knowledge fixed;  and (3) the Structural Consistency Guided Cross-modal Alignment (SCCA) mechanism, which enforces reciprocal structural consistency constraints between visible and infrared modalities in the target domain to identify reliable cross-modal pairs and facilitate robust modality alignment without source supervision. Extensive experiments on benchmark datasets demonstrate that SVIP significantly improves target domain performance and outperforms existing unsupervised VI-ReID approaches under source-free adaptation settings.