Overcoming Dual Incremental Challenges in Continual Person Search via Adapter and Prototype.

Abstract

The advancement of continual person search techniques has seen significant progress in recent years due to its practical applications in the real world. However, continual learning for person search presents significant challenges as it combines both person detection and re-identification (Re-ID) tasks, resulting in issues of domain and class incremental learning. To address these challenges, we propose a novel framework that uses an adapter-based Swin Transformer backbone, and incorporates two key components: Domain Aware Adapter (DAA) blocks and Virtual Prototype Replay-Online Instance Matching (VPR-OIM). Specifically, to solve the domain incremental problem in object detection, we introduce parallel DAA blocks to handle multiple domains, while a Domain Prototype Router (DPR) mechanism is used to dynamically route the feature to the domain-specific adapter. Additionally, for class incremental Re-ID, we extend the OIM loss with virtual prototype replay, which generates Gaussian distribution-based virtual features derived from historical prototypes, effectively enabling the model to preserve knowledge of previous identities while accommodating new identity categories. Overall, our proposed DAA and VPR-OIM simultaneously address the dual incremental challenges of continual person search. Experimental results demonstrate that our method significantly improves both person detection and Re-ID performance in continual learning settings, achieving state-of-the-art (SOTA) performance.