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Vision models are often vulnerable to out-of-distribution (OOD) sampleswithout adapting. While visual prompts offer a lightweight method ofinput-space adaptation for large-scale vision models, they rely on ahigh-dimensional additive vector and labeled data. This leads to overfittingwhen adapting models in a self-supervised test-time setting without labels. Weintroduce convolutional visual prompts (CVP) for label-free test-timeadaptation for robust visual perception. The structured nature of CVP demandsfewer trainable parameters, less than 1\% compared to standard visual prompts,combating overfitting. Extensive experiments and analysis on a wide variety ofOOD visual perception tasks show that our approach is effective, improvingrobustness by up to 5.87% over several large-scale models.

Convolutional Visual Prompt for Robust Visual Perception