Visual Prompt Tuning
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Visual Prompt Tuning. / Jia, Menglin; Tang, Luming; Chen, Bor-Chun; Cardie, Claire; Belongie, Serge; Hariharan, Bharath.
arXiv.org, 2022.Research output: Working paper › Preprint › Research
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TY - UNPB
T1 - Visual Prompt Tuning
AU - Jia, Menglin
AU - Tang, Luming
AU - Chen, Bor-Chun
AU - Cardie, Claire
AU - Belongie, Serge
AU - Hariharan, Bharath
PY - 2022/3/23
Y1 - 2022/3/23
N2 - 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.
AB - 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.
M3 - Preprint
BT - Visual Prompt Tuning
PB - arXiv.org
ER -
ID: 303685576