Semantic Misalignment in Vision-Language Models under Perceptual Degradation
The study tested how visual corruption, like blur or low light, affects Vision-Language Models (VLMs) used for safety tasks. It found that small drops in image quality cause major failures in language understanding, such as making up objects or missing important safety items. This shows that standard image quality scores don't accurately predict if a VLM will make safe decisions.