Not All Progress Transfers: Adaptation-State Reuse for Generalizable Object ReID

Generalizable object re-identification aims to learn identity-discriminative representations that remain effective across heterogeneous object categories and visual distributions. Learning must reconcile fine-grained specialization with unknown matching requirements, although seen-category supervision gives no direct guidance on which adaptation changes to retain for transfer. We identify Progressive Feature Repurposing (PFR): structure beyond an early discriminative span gains seen-category retrieval utility while its category-readout accessibility declines modestly even after refitting. Restoring earlier structure benefits some transfer settings but harms others, with heterogeneous query-level responses: later progress does not uniformly supersede historical utility. Motivated by PFR, we propose ReState (Reusing Adaptation States), treating adaptation history as a reusable representational resource rather than committing to one selected state. During adaptation, ReState retains historical states and composes their feature residuals relative to a pretrained anchor through input- and dimension-conditioned routing. A zero-adaptation option retains access to anchor content, while optimization- and supervision-level regulation shapes alternatives for reuse. Across ten heterogeneous retrieval tasks, ReState demonstrates strong performance against existing ReID methods, with particularly large gains on multi-category benchmarks. It also improves mAP over a strong adaptation baseline on every evaluated category-shifted task.