SANER: Switchable Adapter with Non-parametric Enhanced Routing for Person De-Reidentification

Yimin Liu, Nan Pu, Fengxiang Yang, Wenjing Li, Zhihui Li, Zhun Zhong; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026, pp. 40376-40385

Person De-Reidentification (De-ReID) is an emerging and safety-critical task that aims to selectively forget specific individuals in surveillance systems while preserving the recognition capability for others. Existing methods typically learn both forgetting and retaining objectives within a unified feature space, which leads to conflicting optimization goals and may cause unexpected performance degradation on novel or retained identities. We provide a new perspective to handle De-ReID through feature space decoupling. Although it is a promising solution, discriminating which feature space should be used for the given novel query remain unsolved. To alleviate these challenges, we propose SANER, advancing De-ReID with a Switchable Adapter (SA) and a test-time Non-parametric Enhanced Routing (NER) algorithm. SA decouples the pre-trained feature space into two task-specific subspaces with a forgetting adapter and a retaining adapter. The former suppresses identity-specific semantics for de-identification, while the latter preserves discriminative cues for accurate re-ID. In addition, SA is further enhanced with NER to adaptively analyze optimal feature space routing for the given query at test-time by comparing the query with pre-computed prototypes in the original feature space. Extensive experiments on multiple De-ReID benchmarks demonstrate the effectiveness of SANER, achieving new state-of-the-art De-ReID performance.