Prompt learning in computer vision: a survey

被引:2
|
作者
Lei, Yiming [1 ]
Li, Jingqi [1 ]
Li, Zilong [1 ]
Cao, Yuan [1 ]
Shan, Hongming [2 ,3 ,4 ]
机构
[1] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200438, Peoples R China
[2] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China
[3] Fudan Univ, MOE Frontiers Ctr Brain Sci, Shanghai 200433, Peoples R China
[4] Shanghai Ctr Brain Sci & Brain Inspired Technol, Shanghai 201210, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金; 上海市自然科学基金;
关键词
Prompt learning; Visual prompt tuning (VPT); Image generation; Image classification; Artificial intelligence generated content (AIGC);
D O I
10.1631/FITEE.2300389
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:42 / 63
页数:22
相关论文
共 50 条
  • [1] A Survey of the Application of Deep Learning in Computer Vision
    Liu Yuexia
    Cheng Yunfei
    Wang Wu
    [J]. GLOBAL INTELLIGENCE INDUSTRY CONFERENCE (GIIC 2018), 2018, 10835
  • [2] Hyperbolic Deep Learning in Computer Vision: A Survey
    Mettes, Pascal
    Atigh, Mina Ghadimi
    Keller-Ressel, Martin
    Gu, Jeffrey
    Yeung, Serena
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2024, 132 (09) : 3484 - 3508
  • [3] Incremental learning with neural networks for computer vision: a survey
    Hao Liu
    Yong Zhou
    Bing Liu
    Jiaqi Zhao
    Rui Yao
    Zhiwen Shao
    [J]. Artificial Intelligence Review, 2023, 56 : 4557 - 4589
  • [4] Graph Representation Learning Meets Computer Vision: A Survey
    Jiao, Licheng
    Chen, Jie
    Liu, Fang
    Yang, Shuyuan
    You, Chao
    Liu, Xu
    Li, Lingling
    Hou, Biao
    [J]. IEEE Transactions on Artificial Intelligence, 2023, 4 (01): : 2 - 22
  • [5] Deep reinforcement learning in computer vision: a comprehensive survey
    Le, Ngan
    Rathour, Vidhiwar Singh
    Yamazaki, Kashu
    Luu, Khoa
    Savvides, Marios
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2022, 55 (04) : 2733 - 2819
  • [6] Incremental learning with neural networks for computer vision: a survey
    Liu, Hao
    Zhou, Yong
    Liu, Bing
    Zhao, Jiaqi
    Yao, Rui
    Shao, Zhiwen
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (05) : 4557 - 4589
  • [7] Deep reinforcement learning in computer vision: a comprehensive survey
    Ngan Le
    Vidhiwar Singh Rathour
    Kashu Yamazaki
    Khoa Luu
    Marios Savvides
    [J]. Artificial Intelligence Review, 2022, 55 : 2733 - 2819
  • [8] Visual Prompt Learning: A Survey
    Liao, Ning
    Cao, Min
    Yan, Jun-Chi
    [J]. Jisuanji Xuebao/Chinese Journal of Computers, 2024, 47 (04): : 790 - 820
  • [9] Adversarial Attacks on Deep Learning Models of Computer Vision: A Survey
    Ding, Jia
    Xu, Zhiwu
    [J]. ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2020, PT III, 2020, 12454 : 396 - 408
  • [10] Survey on deep learning based computer vision for sonar imagery
    Steiniger, Yannik
    Kraus, Dieter
    Meisen, Tobias
    [J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 114