Asymmetric Domain Knowledge Distillation for Unsupervised Cross-Domain Person Re-Identification

Unsupervised domain‑adaptive person re‑identification (UDA‑ReID) is plagued by noisy clustering‑derived pseudo‑labels, inherent feature extraction errors from cross‑domain style discrepancies, underutilized outliers, and ineffective source‑domain knowledge transfer in target fine‑tuning. Existing methods either focus on clustering or pseudo‑label refinement while ignoring cross‑domain feature bias, or use contrastive learning hindered by unreliable clustering and marginal value of cross‑domain negative samples. To address these issues in this paper, UDA‑ReID is formulated as an asymmetric domain distillation task, distinct from traditional single‑domain teacher‑student distillation by transferring identity‑related feature clustering from the labeled pre‑trained source to unlabeled target domain. A novel asymmetric domain distillation method is proposed, where identity and domain‑related features are decoupled and recoupled to mitigate domain gaps. And a graph network‑based feature transfer structure is designed for cross‑domain transformation, along with a multi‑domain pseudo‑label refinement strategy leveraging complementary clustering information to generate accurate and noise‑robust target pseudo‑labels. Extensive experiments validate the effectiveness of the proposed framework.