Prompt learning in computer vision: a survey

Yiming LEI , Jingqi LI , Zilong LI , Yuan CAO , Hongming SHAN

Front. Inform. Technol. Electron. Eng ›› 2024, Vol. 25 ›› Issue (1) : 42 -63.

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Front. Inform. Technol. Electron. Eng ›› 2024, Vol. 25 ›› Issue (1) : 42 -63. DOI: 10.1631/FITEE.2300389
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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 visionlanguage 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.

Keywords

Prompt learning / Visual prompt tuning (VPT) / Image generation / Image classification / Artificial intelligence generated content (AIGC)

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Yiming LEI, Jingqi LI, Zilong LI, Yuan CAO, Hongming SHAN. Prompt learning in computer vision: a survey. Front. Inform. Technol. Electron. Eng, 2024, 25(1): 42-63 DOI:10.1631/FITEE.2300389

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