Efficient Transfer Learning for Visual Tasks via Continuous Optimization of Prompts
By Jonathan Conder, Joseph Jefferson, Nathan Pages, Khurram Jawed, Alireza Nejati, Mark Sagar. 2022
10 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Prompting & Reasoning, Representation & Self-Supervised Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Dacheng Tao
- Jitendra Malik
- Trevor Darrell
- Yu Qiao
- Bernt Schiele
- Ming-Hsuan Yang
- Xiaogang Wang
In Representation & Self-Supervised Learning
- Carl Vondrick
- Revant Teotia
- Claire Cardie
- Bharath Hariharan
- Bor-Chun Chen
- Luming Tang
- Xin Eric Wang
- Ser-Nam Lim
Citations in the corpus
- Visual Prompt Tuning (2022, methodology): “Due to the disparity between vision and language modalities, in this paper we ask: can the same method can be applied successfully to image encoders? We are the first work (see related concurrent works [52,62,11,2]) to tackle this question and investigate the generality and feasibility of visual pro”
- Doubly Right Object Recognition: A Why Prompt for Visual Rationales (2022, background): “Due to their lightweight, it has been shown effective for continuous learning [12].”