Review of large vision models and visual prompt engineering

Обзор больших зрительных моделей и визуальной инженерии промптов
Dinggang Shen, Xiang Li, Zhengliang Liu, Songyao Zhang, Yixuan Yuan, Xi Jiang, Shu Zhang, Zihao Wu, Jiaqi Wang, Lin Zhao, Chong Ma, Sigang Yu, Haixing Dai, Qiushi Yang, Yiheng Liu, Enze Shi, Yi Pan, Tuo Zhang, Dajiang Zhu, Bao Ge, Tianming Liu
2023-11-01

artificial general intelligencecomputer visionlarge vision modelsvisual prompt engineeringvisual tasks
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.
1
The growing capabilities of large vision models are increasing the importance of designing task-specific visual prompts.
2
The paper provides a comprehensive and systematic overview of recent visual prompt-engineering advances to support future research.
3
The review summarizes influential large vision models and the prompt-engineering methods used with them in computer vision.
4
Visual prompt engineering is identified as a fundamental methodology for visual and image artificial general intelligence.

large vision models and visual prompt engineering methods in computer vision

methods, applications, and recent advances of visual prompt engineering for specific visual tasks

Publication Details
Publication Date
2023-11-01
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Authors
Dinggang Shen
Xiang Li
Zhengliang Liu
Songyao Zhang
Yixuan Yuan
Xi Jiang
Shu Zhang
Zihao Wu
Jiaqi Wang
Lin Zhao
Chong Ma
Sigang Yu
Haixing Dai
Qiushi Yang
Yiheng Liu
Enze Shi
Yi Pan
Tuo Zhang
Dajiang Zhu
Bao Ge
Tianming Liu
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