RECAP: Relation Evidence Calibration for Detecting Spatial Relation Hallucinations in Vision-Language Models
Vision-language models can answer spatial relation questions confidently even when the image supports an incompatible relation. We formulate relation-grounded selective prediction: accept or reject an already-produced yes/no answer by auditing its visual support, rather than treating uncertainty as evidence. RECAP, our relation-evidence calibration framework, compares image-conditioned likelihoods for a claim, its semantic contradictions, and optional one-sided supports, then converts these witnesses into an answer-conditioned rejection risk. A calibration-only gate preserves confidence as a veto when confidence is demonstrably informative and otherwise deploys relation evidence alone. Ac...
| Authors | Feixiang Liu, Qiang Qiu, Qingyang Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 August 2026 |
| Last updated at source | 4 August 2026 |
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