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Textual prompt tuning has demonstrated significant performance improvementsin adapting natural language processing models to a variety of downstream tasksby treating hand-engineered prompts as trainable parameters. Inspired by thesuccess of textual prompting, several studies have investigated the efficacy ofvisual prompt tuning. In this work, we present Visual Prompt Adaptation (VPA),the first framework that generalizes visual prompting with test-timeadaptation. VPA introduces a small number of learnable tokens, enabling fullytest-time and storage-efficient adaptation without necessitating source-domaininformation. We examine our VPA design under diverse adaptation settings,encompassing single-image, batched-image, and pseudo-label adaptation. Weevaluate VPA on multiple tasks, including out-of-distribution (OOD)generalization, corruption robustness, and domain adaptation. Experimentalresults reveal that VPA effectively enhances OOD generalization by 3.3% acrossvarious models, surpassing previous test-time approaches. Furthermore, we showthat VPA improves corruption robustness by 6.5% compared to strong baselines.Finally, we demonstrate that VPA also boosts domain adaptation performance byrelatively 5.2%. Our VPA also exhibits marked effectiveness in improving therobustness of zero-shot recognition for vision-language models.

VPA: Fully Test-Time Visual Prompt Adaptation