Deep learning for visual understanding: A review

被引：1447

作者：

Guo, Yanming ^{[1
,3
]}

Liu, Yu ^{[1
]}

Oerlemans, Ard ^{[2
]}

Lao, Songyang ^{[3
]}

Wu, Song ^{[1
]}

Lew, Michael S. ^{[1
]}

机构：

[1] Leiden Univ, LIACS Media Lab, Niels Bohrweg 1, Leiden, Netherlands

[2] VDG Secur BV, Zoetermeer, Netherlands

[3] Natl Univ Def Technol, Coll Informat Syst & Management, Changsha, Hunan, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 187卷

关键词：

Deep learning; Computer vision; Developments; Applications; Trends; Challenges; NEURAL-NETWORKS; REPRESENTATIONS;

D O I：

10.1016/j.neucom.2015.09.116

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Deep learning algorithms are a subset of the machine learning algorithms, which aim at discovering multiple levels of distributed representations. Recently, numerous deep learning algorithms have been proposed to solve traditional artificial intelligence problems. This work aims to review the state-of-the-art in deep learning algorithms in computer vision by highlighting the contributions and challenges from over 210 recent research papers. It first gives an overview of various deep learning approaches and their recent developments, and then briefly describes their applications in diverse vision tasks, such as image classification, object detection, image retrieval, semantic segmentation and human pose estimation. Finally, the paper summarizes the future trends and challenges in designing and training deep neural networks. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：27 / 48

页数：22