PROVE: Probabilistic Visual Grounding via Patch-Level Evidence for Vision-Language Models

Visual grounding with vision-language models such as CLIP typically produces a single deterministic relevance map for an image-query pair, without indicating whether the localisation is reliable or whether the referent is present at all. We introduce \textbf{PROVE} (\textbf{Pro}babilistic \textbf{V}isual Grounding via Patch-Level \textbf{E}vidence), a post-hoc evidential adapter that maps frozen CLIP patch features to per-patch Beta distributions without modifying the backbone. PROVE casts grounding as per-patch membership: each patch accumulates positive and negative evidence for whether it belongs to the referent, so a query may match zero, one, or many patches. We train PROVE with an evidential objective on soft patch-coverage targets, and define a prior-pull objective that keeps absent-referent examples near an uninformative prior, so that vacuity serves as an absence signal without a dedicated presence classifier. During inference, a single forward pass yields, in closed form, a posterior-mean grounding map, a spatial vacuity map, and an image-level absence score. We evaluate PROVE on referring-expression benchmarks: it is competitive in localisation with state-of-the-art CLIP grounding methods, its uncertainty tracks localisation error and supports selective prediction under abstention, and its image-level score detects when the referent is absent altogether. Our evidential patch-membership modelling offers a simple, drop-in mechanism for probabilistic verification in frozen vision-language grounding systems.