  The current modus operandi in adapting pre-trained models involves updating
all the backbone parameters, ie, full fine-tuning. This paper introduces Visual
Prompt Tuning (VPT) as an efficient and effective alternative to full
fine-tuning for large-scale Transformer models in vision. Taking inspiration
from recent advances in efficiently tuning large language models, VPT
introduces only a small amount (less than 1% of model parameters) of trainable
parameters in the input space while keeping the model backbone frozen. Via
extensive experiments on a wide variety of downstream recognition tasks, we
show that VPT achieves significant performance gains compared to other
parameter efficient tuning protocols. Most importantly, VPT even outperforms
full fine-tuning in many cases across model capacities and training data
scales, while reducing per-task storage cost.
