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原文摘要

Lifelong Person Re-Identification aims to adapt to new domains while preserving old knowledge. Existing methods, whether distillation-based or rehearsal-based, attempt to consolidate diverse knowledge within a fixed model architecture. However, the limited adaptability of such architectures often leads to catastrophic forgetting by overwriting previously acquired knowledge. To overcome this limitation, we propose Versatile Incremental Adaptation (VIA), a novel dynamical expansion framework for LReID, which unleashes restricted knowledge during continuous learning by large pre-trained models. Specifically, Unseen-domain person Adapter (UnA) is embedded in VIA, which employs incremental modular learning to capture the domain's specific knowledge, thereby reducing cross-domain interference and releasing task-specific capacity that is previously limited by static parameter sharing. Meanwhile, considering the substantial amount of sharing knowledge across domains in LReID, we design the Domain-aware Dispatch module to enable inter-domain collaboration and knowledge reuse through adaptive cooperation among multiple shared adapters. Besides, a Holistic Domain Controller is designed to dynamically regulate the learning capacity for new domains based on knowledge similarity, thereby effectively unleashing the generalization potential of pre-trained models. Furthermore, a lightweight Similarity-Guided Auto-Selector is proposed to assign inputs to relevant adapters during inference automatically. Extensive experiments validate VIA's effectiveness, surpassing state-of-the-art methods in both seen and unseen domains.