Breaking the Paired Sample Barrier in Person Re-Identification: Leveraging Unpaired Samples for Domain Generalization

Domain generalization (DG) for person re-identification (Re-ID) aims to train models on labeled source domains that generalize well to unseen target domains. However, DG for Re-ID faces a major challenge: existing methods rely solely on labeled paired samples to train DG models and are unable to effectively leverage unpaired samples across cameras. In many cases, cross-camera paired samples are extremely scarce and difficult to annotate. To overcome this limitation, we introduce a novel method specifically tailored for Re-ID. This method leverages cross-camera unpaired samples in model training, thereby reducing the dependence on cross-camera paired samples. We refer to this technique as Unpaired-driven DG (U-DG) person Re-ID. The proposed method leverages a robust image encoder to extract identity-consistent features across various camera views. This capability is further enhanced by integrating a multi-camera person identity classifier, which boosts the encoder's ability to capture consistent identities, even when viewed from different camera perspectives. To address the scarcity of cross-camera paired samples, we devise a unique model training strategy in our method. Specifically, we use the feature vector from the person identity classifier as a single identity prototype. This prototype serves as a reference for generating identity-related prompts across cameras, effectively compensating for the scarcity of cross-camera paired samples during model training. Additionally, we employ a learnable perturbation prompt to mimic appearance variations exhibited by the same individual across different cameras. Our U-DG offers numerous advantages: it can effectively leverage a large number of unpaired samples for model training, compensating for the scarcity of cross-camera paired samples. Moreover, it does not rely solely on cross-camera paired samples, thereby facilitating the construction of training samples. Experimental results on multiple challenging datasets demonstrate that our approach achieves performance comparable to typical DG person Re-ID, highlighting its feasibility and effectiveness.