SANER：用于行人去重识别的可切换适配器与非参数增强路由
SANER: Switchable Adapter with Non-parametric Enhanced Routing for Person De-Reidentification
提出可切换适配器与测试时非参数路由，将预训练特征空间解耦为遗忘与保留子空间，为行人重识别中的选择性遗忘提供可复用方案。
原文依据 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.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论行人去重识别（De-ReID）是一项新兴且安全关键的任务，旨在监控系统中选择性地遗忘特定个体，同时保留对其他个体的识别能力。现有方法通常在一个统一特征空间中同时学习遗忘和保留目标，这导致优化目标冲突，并可能对新颖或保留身份造成意外性能下降。我们通过特征空间解耦为处理De-ReID提供了一个新视角。尽管这是一个有前景的解决方案，但如何判别给定新颖查询应使用哪个特征空间仍未解决。为缓解这些挑战，我们提出SANER，通过可切换适配器（SA）和测试时非参数增强路由（NER）算法推进De-ReID。SA将预训练特征空间解耦为两个任务特定子空间：遗忘适配器和保留适配器。前者抑制身份特定语义以实现去识别，后者保留判别线索以进行准确重识别。此外，SA进一步通过NER增强，在测试时通过将查询与原始特征空间中预计算的原型进行比较，自适应地分析给定查询的最优特征空间路由。在多个De-ReID基准上的大量实验证明了SANER的有效性，实现了新的最优De-ReID性能。
英文原文摘要 1442 字符
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.