Routable Parameter Memories for Exemplar-Free Lifelong Person Re-Identification

Lifelong person re-identification (LReID) aims to continuously learn from sequential domains while preserving prior identity-discriminative knowledge. However, substantial inter-domain gaps make the model prone to overwriting earlier representations when adapting to new domains, resulting in catastrophic forgetting. Existing methods typically preserve historical knowledge through image replay, knowledge distillation, or distribution rehearsal, but they respectively face the burden of retaining historical data, insufficient activation of old-domain discriminative cues under substantial domain shifts, and difficulty in directly preserving previously learned identity-discriminative transformations through reconstructed historical visual statistics. To overcome these limitations, we propose Routable Parameter Memories (RPM), an exemplar-free LReID framework built on a parameter-memory perspective. Instead of replaying historical images, previous-model responses, or proxy distributions, RPM directly treats the discriminative parameter updates learned from each domain as parameter memories and draws on biological memory processes in which pattern separation reduces interference among similar or overlapping memory traces and cue-driven pattern completion reinstates relevant memories from partial cues. However, storing parameter memories alone does not determine how new knowledge should be written or how stored knowledge should be read for each input. In this way, the lifelong learning process is organized into the writing, storage, and reading of parameter memories. Specifically, Subspace-Guided Writing uses historical activation subspaces to guide the writing of new memories with reduced overlap, after which the learned domain-specific LoRA updates are frozen and sequentially stored in a parameter memory bank. Finally, during inference, Prototype-Guided Reading uses prototype anchors as retrieval cues to adaptively read and compose relevant memories for each input. Extensive experiments show that RPM outperforms existing methods by up to 5.9% on seen domains and 4.4% on unseen domains, respectively.