EVEE: Event-Based Online Adaptation for Matching on Unknown Targets

Offline-trained detectors and matchers often degrade in de-ployment when encountering previously unknown targets, since offlinetraining cannot anticipate the appearance and structural variations ofunknown targets. We propose EVEE, a feature adaptation frameworkthat leverages temporally accumulated event evidence as test-time proxysupervision for adapting detection and matching to previously unseentargets. Rather than performing fully asynchronous event-level match-ing or using events merely as auxiliary inputs, EVEE constructs event-derived proxy supervision to drive label-free online updates. EVEE con-tains three components. (i) Training Warehouse (TraW) accelerates con-vergence within a limited observation window via multi-threaded updatesand best-weight broadcasting. (ii) Event Voting Bank (EVB) accumu-lates bidirectional positive/negative votes to separate foreground frombackground, thereby reducing the impact of background noise on themodel. (iii) ReWeight improves correspondence reliability by rescoringcandidates using appearance similarity, geometric consistency, and EVBpriors. Experiments on public datasets and a real RGB–event platform,show that EVEE achieves 1.9% higher average pose ACC/AUC on un-known targets than strong baselines. Meanwhile, it runs at 51.3 FPSfor matching and 14.52 FPS for online adaptation. The code is publiclyavailable at https://github.com/ZeJZhao/EVEE.