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

通过引入可学习的标记，VPA（Visual Prompt Adaptation）作为一个框架通过测试时间的自适应实现了视觉提示的普遍性，且不需要源领域信息，实验结果表明 VPA 有效提高了各种模型的历程泛化、抗干扰性和领域适应能力，以及对视觉 - 语言模型的零样本识别性能的鲁棒性改进。