Review of large vision models and visual prompt engineering

Jiaqi Wang , Zhengliang Liu , Lin Zhao , Zihao Wu , Chong Ma , Sigang Yu , Haixing Dai , Qiushi Yang , Yiheng Liu , Songyao Zhang , Enze Shi , Yi Pan , Tuo Zhang , Dajiang Zhu , Xiang Li , Xi Jiang , Bao Ge , Yixuan Yuan , Dinggang Shen , Tianming Liu , Shu Zhang
{"title":"Review of large vision models and visual prompt engineering","authors":"Jiaqi Wang ,&nbsp;Zhengliang Liu ,&nbsp;Lin Zhao ,&nbsp;Zihao Wu ,&nbsp;Chong Ma ,&nbsp;Sigang Yu ,&nbsp;Haixing Dai ,&nbsp;Qiushi Yang ,&nbsp;Yiheng Liu ,&nbsp;Songyao Zhang ,&nbsp;Enze Shi ,&nbsp;Yi Pan ,&nbsp;Tuo Zhang ,&nbsp;Dajiang Zhu ,&nbsp;Xiang Li ,&nbsp;Xi Jiang ,&nbsp;Bao Ge ,&nbsp;Yixuan Yuan ,&nbsp;Dinggang Shen ,&nbsp;Tianming Liu ,&nbsp;Shu Zhang","doi":"10.1016/j.metrad.2023.100047","DOIUrl":null,"url":null,"abstract":"<div><p>Visual prompt engineering is a fundamental methodology in the field of visual and image artificial general intelligence. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.</p></div>","PeriodicalId":100921,"journal":{"name":"Meta-Radiology","volume":"1 3","pages":"Article 100047"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2950162823000474/pdfft?md5=837283e184272b93d845542b4edd9c07&pid=1-s2.0-S2950162823000474-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Meta-Radiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950162823000474","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Visual prompt engineering is a fundamental methodology in the field of visual and image artificial general intelligence. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.

大型视觉模型和视觉提示工程回顾
视觉提示工程是视觉和图像人工通用智能领域的一种基本方法。随着大型视觉模型的发展,提示工程的重要性日益凸显。为特定视觉任务设计合适的提示已成为一个有意义的研究方向。本综述旨在总结计算机视觉领域中用于大型视觉模型和视觉提示工程的方法,探索视觉提示工程的最新进展。我们介绍了视觉领域有影响力的大型模型,以及在这些模型上使用的一系列提示工程方法。我们希望这篇综述能够全面系统地描述基于大型视觉模型的提示工程方法,为未来研究人员探索这一领域提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信