When Samples Are Gone: Keeping Past Knowledge at Work for Lifelong Person Re-Identification

Lifelong person re-identification aims to learn from sequential domains while maintaining retrieval performance on previously learned domains after historical samples become unavailable. Current LReID approaches mainly rely on replaying historical samples, transferring prior-model behavior, or reconstructing proxy distributions, three strategies that respectively require access to stored data, depend on current inputs to sufficiently activate old-domain behavior, or preserve historical knowledge only indirectly through proxy observations rather than the learned transformations themselves. To address these limitations, we propose Keeping Past Knowledge at Work (KPKW), an exemplar-free LReID framework that directly retains each domain's learned transformations together with statistical information to guide their subsequent use. KPKW first retains the identity-discriminative transformations learned from each domain as frozen LoRA updates. However, retaining these updates alone does not preserve the historical information needed to guide their later use when jointly applied in a unified encoder. KPKW therefore records layer-wise input second moments and between-class scatter for each frozen update without storing historical samples. The former enables historical response preservation, while the latter provides identity-related directions for subsequent optimization. Guided by these statistics, KPKW learns channel-wise composition coefficients with historical response and output-structure preservation, while soft gradient adjustment further reduces interference with historical identity-related directions. During inference, the learned channel organization coordinates the accumulated frozen updates within a unified encoder, enabling identity representation and retrieval without domain labels. Extensive experiments show that KPKW outperforms existing methods by up to 3.4% on seen domains and 4.8% on unseen domains.