Prompt learning in computer vision: a survey

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Abstract

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. 自大型预训练视觉—语言模型(VLM)爆发以来,提示学习已在计算机视觉领域引发广泛关注。基于VLM构建的视觉和语言信息之间的密切关系,提示学习成为许多重要应用领域(如人工智能内容生成(AIGC))中的关键技术。本综述循序渐进且全面地总结了与AIGC相关的视觉提示学习。首先介绍了VLM,它是视觉提示学习的基础。然后,回顾了视觉提示学习方法和提示引导生成模型,并讨论了如何提高将AIGC模型适用于下游特定任务的效率。最后,提供了一些有前景的关于提示学习的研究方向。