Do Vision-Language Foundational models show Robust Visual Perception?
This work addresses the reliability of vision-language models for real-world applications, highlighting a critical limitation in their generalization capabilities.
The study investigates the robustness of vision-language foundational models to distribution shifts like motion blur and fog, finding that their performance significantly degrades under such corruptions compared to human perception.
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}