Vision-Language Models Show High Confidence Despite Accuracy Drop in Degraded Visibility

A new study published on arXiv investigates the robustness of vision-language models (VLMs) by testing their performance under degraded visibility conditions. Researchers measured accuracy, confidence levels, and the models' ability to abstain when faced with fog and underwater environments, using a physical visibility axis in meters. The findings indicate that VLMs often maintain high confidence even when their accuracy drops significantly, revealing a substantial blind spot in their perception. AI

IMPACT Highlights a critical gap in VLM reliability, suggesting current models may overstate their understanding in real-world, degraded conditions.

RANK_REASON The cluster contains an academic paper detailing novel research findings on VLM robustness. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• COCO
• Hugging Face
• ImageNet-C
• Koschmieder model
• Mochamad Rizal Fauzan
• vision-language models

• paper
• safety

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →