Abstract
The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, i.e., 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. Code is available at github.com/kmnp/vpt.
M. Jia and L. Tang—Equal contribution.
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Notes
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Some Transformer architectures in Vision such as Swin [39] do not use \(\texttt {[CLS]}\) and treat global pooled \(\textbf{E}_N\) as input for Head. We follow their designs when adapting VPT to these Transformer variants. See the Appendix for more details.
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Acknowledgement
Menglin is supported by a Meta AI research grant awarded to Cornell University, Luming and Bharath is supported by NSF IIS-2144117, Serge is supported in part by the Pioneer Centre for AI, DNRF grant number P1. We would like to thank Alexander Rush, Yin Cui for valuable suggestions and discussion.
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Jia, M. et al. (2022). Visual Prompt Tuning. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13693. Springer, Cham. https://doi.org/10.1007/978-3-031-19827-4_41
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