A Brief Survey on Semantic Segmentation with Deep Learning

被引:308
|
作者
Hao, Shijie [1 ,2 ]
Zhou, Yuan [1 ,2 ]
Guo, Yanrong [1 ,2 ]
机构
[1] Hefei Univ Technol, Key Lab Knowledge Engn Big Data, Minist Educ, Hefei 230009, Peoples R China
[2] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Peoples R China
关键词
NEURAL-NETWORK; REGRESSION; IMAGES; MODEL; VIDEO;
D O I
10.1016/j.neucom.2019.11.118
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Semantic segmentation is a challenging task in computer vision. In recent years, the performance of semantic segmentation has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed. This paper aims to provide a brief review of research efforts on deep-learning-based semantic segmentation methods. We categorize the related research according to its supervision level, i.e., fully-supervised methods, weakly-supervised methods and semi-supervised methods. We also discuss the common challenges of the current research, and present several valuable growing research points in this field. This survey is expected to familiarize readers with the progress and challenges of semantic segmentation research in the deep learning era. © 2020 Elsevier B.V.
引用
收藏
页码:302 / 321
页数:20
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