Cross-Domain Robustness of Vision-Language Models on Perturbed Medical and Autonomous Driving Benchmarks

Abstract

Abstract: Medical image segmentation allows quantifying target structure size and shape, aiding in disease diagnosis, prognosis, surgery planning, and comprehension.Building upon recent advancements in foundation Vision-Language Models (VLMs) from natural image-text pairs, several studies have proposed adapting them to Vision-Language Segmentation Models (VLSMs) that allow using language text as an additional input to segmentation models. Introducing auxiliary information via text with human-in-the-loop prompting during inference opens up unique opportunities, such as open vocabulary segmentation and po

Research Question

How do vision-language models perform in cross-domain robustness evaluations when tested on perturbed multimodal benchmarks from domains like medical imaging or autonomous driving, using metrics such as BLEU score for captioning and AUC-ROC for authenticity detection?

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