Correcting the Selector: Training-Free Proposal-Field Calibration for Open-Vocabulary Detection

A frozen open-vocabulary detector can already contain a correct candidate and still fail to return it. Adding labels can evict an unchanged detection from a finite output list; within a class, confidence can favor a poorly localized box over a better alternative. We identify the selector as a distinct intervention point: contextual and geometric relations remain available in the dense proposal field even when scalar-score selection discards them. We introduce VoPR, a training-free correction of the proposal field for fixed-vocabulary accuracy before the unchanged selector. Proposal-Conditioned Vocabulary Routing (PVR) adjusts category admission, and Candidate Quality Calibration (CQC) combines semantic competition, frozen distributional box redecoding, and proposal agreement to refine candidate ordering. For class-separable scoring, we localize vocabulary coupling to shared finite caps. Under fixed-order selection, class-broadcast routing admits only a prefix of each class's NMS-survivor sequence; candidate-specific calibration supplies the complementary ability to change the sequence itself. VoPR improves WeDetect Tiny/Base/Large by \(+0.35/+0.24/+0.20\) AP on COCO; frozen profiles also improve all nine LVIS and COCO-O transfer settings. With distractor labels kept active, VoPR gains \(+0.344\)–\(+0.637\) target-label AP under held-out distractor content. These paired results connect class admission and candidate refinement to improved detection without additional training, parameter updates, or another visual forward pass.