Exploring Visual Prompts for Adapting Large-Scale Models
We investigate the efficacy of visual prompting to adapt large-scale models in vision. Following the recent approach from prompt tuning and adversarial reprogramming, we learn a single image perturbation such that a frozen model prompted with this perturbation performs a new task. Through comprehensive experiments, we demonstrate that visual prompting is particularly effective for CLIP and robust to distribution shift, achieving performance competitive with standard linear probes. We further analyze properties of the downstream dataset, prompt design, and output transformation in regard to adaptation performance. The surprising effectiveness of visual prompting provides a new perspective on adapting pre-trained models in vision. Code is available at http://hjbahng.github.io/visual_prompting .
PDF Abstract
CIFAR-10
CIFAR-100
SVHN
UCF101
Oxford 102 Flower
DTD
Food-101
CLEVR
EuroSAT
Wilds
RESISC45
fMoW
