Frequency-Structured Response Consolidation for Lifelong Person Re-Identification

Lifelong person re-identification (LReID) learns from new domains while retaining previously acquired knowledge, with catastrophic forgetting remaining its central challenge. Existing data replay methods raise privacy concerns by storing historical exemplars, while knowledge distillation methods face cumulative forgetting of undistilled knowledge. Historical style replay transfers previous domain styles to current images, producing paired current-style and historical-style views and thereby avoiding such privacy concerns. However, since domain styles only partially represent previous domains, only limited historical knowledge can be recovered at each stage. Subsequent domain learning may progressively weaken earlier-domain knowledge, leading to catastrophic forgetting. To address this problem, we propose Frequency-Structured Response Consolidation (FSRC), which combines structured frequency modulation with cross-style response alignment to promote knowledge reuse and consolidation. Specifically, Structured Frequency Modulation (SFM) is embedded into the model to selectively regulate frequency-dependent changes in feature responses induced by historical-style variations, allowing the model to adapt to evolving domain styles and facilitating cross-domain knowledge reuse. Meanwhile, we introduce Cross-Style Alignment (CSA) to align paired feature responses of the same pedestrian under current and historical styles, thereby constraining subsequent representation updates to reduce interference with previously acquired knowledge and support knowledge consolidation. Extensive experiments on the standard LReID benchmark under two training orders demonstrate that FSRC surpasses state-of-the-art methods in both seen and unseen domains.