VPA: Fully Test-Time Visual Prompt Adaptation
Jiachen Sun Mark Ibrahim Melissa Hall Ivan Evtimov Z. Morley Mao Cristian Canton Ferrer Caner Hazirbas
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.