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 in-creasingly 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 cate-gorized 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 su-pervised 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.
万方数据
维普
