In this episode we discuss
VPA: Fully Test-Time Visual Prompt Adaptation
by Jiachen Sun, Mark Ibrahim, Melissa Hall, Ivan Evtimov, Z. Morley Mao, Cristian Canton Ferrer, Caner Hazirbas. The paper presents Visual Prompt Adaptation (VPA), a framework that extends prompt tuning to visual recognition tasks. VPA allows for test-time adaptation without source-domain information and improves out-of-distribution generalization, corruption robustness, domain adaptation, and zero-shot recognition. Experimental results show improvements of 3.3% in OOD generalization, 6.5% in corruption robustness, and 5.2% in domain adaptation.
