Prompt learning in computer vision: a survey

被引:2
|
作者
Lei, Yiming [1 ]
Li, Jingqi [1 ]
Li, Zilong [1 ]
Cao, Yuan [1 ]
Shan, Hongming [2 ,3 ,4 ]
机构
[1] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200438, Peoples R China
[2] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China
[3] Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China
[4] Shanghai Ctr Brain Sci & Brain Inspired Technol, Shanghai 201210, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金; 上海市自然科学基金;
关键词
Prompt learning; Visual prompt tuning (VPT); Image generation; Image classification; Artificial intelligence generated content (AIGC);
D O I
10.1631/FITEE.2300389
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Prompt learning has attracted broad attention in computer vision since the large pre-trained vision-language models (VLMs) exploded. Based on the close relationship between vision and language information built by VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligence generated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual prompt learning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, we review the vision prompt learning methods and prompt-guided generative models, and discuss how to improve the efficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising research directions concerning prompt learning.
引用
收藏
页码:42 / 63
页数:22
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