# Do Vision-Language Foundational models show Robust Visual Perception? Source: [Do Vision-Language Foundational models show Robust Visual Perception?]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:09:38.833Z Authors: Shivam Chandhok, Pranav Tandon Published: 2024-08-13T10:06:53.000Z ## Abstract Recent advances in vision-language foundational models have enabled development of systems that can perform visual understanding and reasoning tasks. However, it is unclear if these models are robust to distribution shifts, and how their performance and generalization capabilities vary under changes in data distribution. In this project we strive to answer the question "Are vision-language foundational models robust to distribution shifts like human perception?" Specifically, we consider a diverse range of vision-language models and compare how the performance of these systems is affected by corruption based distribution shifts (such as \\textit{motion blur, fog, snow, gaussian noise}) commonly found in practical real-world scenarios. We analyse the generalization capabilities qualitatively and quantitatively on zero-shot image classification task under aforementioned distribution shifts. Our code will be avaible at \\url{https://github.com/shivam-chandhok/CPSC-540-Project} ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()