{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/visual-prompt-tuning","title":"Visual Prompt Tuning","arxiv_id":"2203.12119","date":"2022-03-23","proceeding":null,"authors":["Menglin Jia","Luming Tang","Bor-Chun Chen","Claire Cardie","Serge Belongie","Bharath Hariharan","Ser-Nam Lim"],"abstract":"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. 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