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Exploring Visual Prompts for Adapting Large-Scale Models
[article]
2022
arXiv
pre-print
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
arXiv:2203.17274v2
fatcat:tkmhhqgt6vdz7igktf6sr2tpq4