QuEST: Quality-guided Evidence Selection and Temporal State Control for Event-based Object Detection

Event activity reveals where changes occur, but its magnitude alone does not determine their relevance to object detection. Recurrent sparse detectors must therefore decide both which observations receive computation and how strongly they update temporal memory. We introduce QuEST (Quality-guided Evidence Selection and Temporal State Control), a lightweight controller that coordinates these decisions through a shared, detection-supervised quality field. Predicted from explicit event statistics, this field guides fixed-cardinality token selection, modulates selective state-space integration, and regulates cross-frame recurrent writing. Foreground-preserved consistency training regularizes predictions and control signals under changes in background event statistics, while retaining a single recurrent stream at inference. On the full Gen1 streaming test set, QuEST achieves 52.02 AP at 50% token retention, improving over an architecture-matched activity-only baseline by 1.30 AP and 1.88 AP₇₅ at identical per-stage scan cardinality, with 1.57% additional parameters and 6.02% additional backbone FLOPs. Without additional pretraining, QuEST also achieves 50.6 AP on 1Mpx and 33.2 AP on eTraM. Its AP advantage over the activity-only baseline persists across all five evaluated event-statistic shifts. These results support shared quality guidance as an effective criterion for coordinating sparse computation and temporal memory in event-based detection.