Due to the increase in computational resources and accessibility of data, an increase in large, deep learning models trained on copious amounts of data using self-supervised or semi-supervised learning have emerged. These "foundation" models are often adapted to a variety of downstream tasks like classification, object detection, and segmentation with little-to-no training on the target dataset. In this work, we perform a robustness analysis of Visual Foundation Models (VFMs) for segmentation tasks and compare them to supervised models of smaller scale. We focus on robustness against real-world distribution shift perturbations.We benchmark four state-of-the-art segmentation architectures using 2 different datasets, COCO and ADE20K, with 17 different perturbations with 5 severity levels each. We find interesting insights that include (1) VFMs are not robust to compression-based corruptions, (2) while the selected VFMs do not significantly outperform or exhibit more robustness compared to non-VFM models, they remain competitively robust in zero-shot evaluations, particularly when non-VFM are under supervision and (3) selected VFMs demonstrate greater resilience to specific categories of objects, likely due to their open-vocabulary training paradigm, a feature that non-VFM models typically lack. We posit that the suggested robustness evaluation introduces new requirements for foundational models, thus sparking further research to enhance their performance.

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