Computer Science ›› 2024, Vol. 51 ›› Issue (11): 112-132.doi: 10.11896/jsjkx.231100089

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Review of Visual Representation Learning

WANG Shuaiwei1, LEI Jie1, FENG Zunlei2, LIANG Ronghua1   

  1. 1 College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
    2 College of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China
  • Received:2023-11-14 Revised:2024-04-11 Online:2024-11-15 Published:2024-11-06
  • About author:WANG Shuaiwei,born in 1999,postgraduate.His main research interests include few-shot video segmentation and visual representation learning.
    LEI Jie,born in 1991,assistant professor.His main research interests include deep network optimization and visual representation learning。
  • Supported by:
    National Natural Science Foundation of China(62106226,62036009) and Natural Science Foundation of Zhejiang Province,China(LQ22F020013,LDT23F0202).

Abstract: Representation learning is an important step of artificial intelligence algorithm,where well designed representation can boost downstream tasks.With the development of deep learning in computer vision,visual representation learning has become increasingly important,aiming at transforming complex visual information into representation that is easier for artificial intelligence algorithm to learn.In this paper,we focus on current research works widely used in visual representation learning,which are categorized as pre-trained visual representation learning,generative visual representation learning,contrastive visual representation learning,decoupled visual representation learning,and visual representation learning combined with language information accor-ding to the degrees and types of data dependency.Specifically,pre-trained visual representation learning is the application of supervised pre-training model in visual representation learning;generative visual representation learning uses generative model to learn visual representations;and contrastive visual representation learning focuses on the various network frameworks which using contrast learning to learn visual representations.Besides,the paper presents the applications of VAE and GAN in decoupled visual representation learning,as well as various approaches to improve visual representation learning with language information.Finally,evaluation metrics in visual representation learning and future perspectives are summarized.

Key words: Visual representation learning, Artificial intelligence algorithm, Decoupled visual representation learning, Language information

CLC Number: 

  • TP391
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