Survey of recent progress in semantic image segmentation with CNNs

作     者：Qichuan GENG Zhong ZHOU Xiaochun CAO 

作者机构：State Key Laboratory of Virtual Reality Technology and SystemsSchool of Computer Science and Engineering Beihang University State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2018年第61卷第5期

页      面：107-124页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by National High-tech R&D Program of China (863 Program) (Grant No. 2015AA016403) National Natural Science Foundation of China (Grant Nos. 61572061, 61472020) 

主　　题：semantic image segmentation CNN Pascal VOC 2012 challenge multi-granularity features construction of contextual relationships 

摘      要：In recent years, convolutional neural networks(CNNs) are leading the way in many computer vision tasks, such as image classification, object detection, and face recognition. In order to produce more refined semantic image segmentation, we survey the powerful CNNs and novel elaborate layers, structures and strategies, especially including those that have achieved the state-of-the-art results on the Pascal VOC 2012 semantic segmentation challenge. Moreover, we discuss their different working stages and various mechanisms to utilize the structural and contextual information in the image and feature spaces. Finally, combining some popular underlying referential methods in homologous problems, we propose several possible directions and approaches to incorporate existing effective methods as components to enhance CNNs for the segmentation of specific semantic objects.