Title: Domain Generalizable Person Re-identification via Adversarial Dual-Stream Strategy with Local Consistency
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Person re-identification, domain generalization, local commonalities mining, adversarial local perturbation

Abstract: Domain Generalizable Person Re-identification (DG Re-ID) faces significant challenges due to appearance variations across different environments, resulting in domain shifts when models are deployed on unseen target domains. Current methods often neglect shared structural commonalities across identities, which limits their ability to generalize and recognize fine-grained identity details effectively. To address these issues, we propose an Adversarial Dual-Stream Learning (ADSL) framework, which integrates two complementary strategies: mining stable local commonalities and modeling local perturbations. The Cross-Identity Local Consistency Learning (CILL) module builds a memory bank of local features and utilizes clustering-driven similarity learning to balance structural consistency and discriminative granularity. Simultaneously, the Dual-stream Adversarial Perturbation Strategy (DAPS) generates adversarial samples that simulate cross-domain appearance variations while preserving local semantic structures. To further improve robustness to domain shifts, we introduce a Clean-Adv Local Cosine Alignment constraint, which ensures feature consistency between clean and adversarial samples in the local semantic space. Extensive experiments on DG Re-ID benchmarks demonstrate that our method significantly outperforms existing state-of-the-art approaches, highlighting its effectiveness and superiority.