摘要
Prompt learning has attracted broad attention in computer vision since the large pre-trained visionlanguagemodels (VLMs) exploded. Based on the close relationship between vision and language information builtby VLM, prompt learning becomes a crucial technique in many important applications such as artificial intelligencegenerated content (AIGC). In this survey, we provide a progressive and comprehensive review of visual promptlearning as related to AIGC. We begin by introducing VLM, the foundation of visual prompt learning. Then, wereview the vision prompt learning methods and prompt-guided generative models, and discuss how to improve theefficiency of adapting AIGC models to specific downstream tasks. Finally, we provide some promising researchdirections concerning prompt learning.
基金
Project supported by the National Natural Science Foundation of China(Nos.62306075 and 62101136)
the China Postdoctoral Science Foundation(No.2022TQ0069)
the Natural Science Foundation of Shanghai,China(No.21ZR1403600)
the Shanghai Municipal of Science and Technology Project,China(No.20JC1419500)
the Shanghai Center for Brain Science and Brain-Inspired Technology,China。