Attention-Guided Network for Semantic Video Segmentation

doi:10.1109/ACCESS.2019.2943365

	Attention-Guided Network for Semantic Video Segmentation
	Li, Jiangyun 1,2; Zhao, Yikai 1,2; Fu, Jun3 ; Wu, Jiajia 4; Liu, Jing3
发表期刊	IEEE ACCESS
ISSN	2169-3536
	2019
卷号	7 页码:140680-140689
通讯作者	Li, Jiangyun(leejy@ustb.edu.cn)
摘要	Remarkable success has been made by deep convolutional neural network (CNN) models in semantic image segmentation. However, most segmentation models are based on classification networks which tend to learn image-level features and lost abundant spatial information due to repeated pooling and downsampling operations, and the CNN-based methods are not robust to inputs, hence directly applying existing segmentation methods to semantic video segmentation will result in spatially inconsecutive and temporally inconsistent segmentation predictions within one instance and of the same objects across adjacent frames, respectively. To tackle this challenge, we propose an Attention-Guided Network (AGNet) to adaptively strengthen inter-frame and intra-frame features for more precise segmentation predictions. Specifically, we append an adjacent attention module (AAM) and a spatial attention module (SAM) on the top of dilated fully convolutional network (FCN), which model the feature correlations in temporal and spatial dimensions, respectively. The AAM selectively enhances the inter-frame features of the same objects across adjacent frames for temporally consistent predictions. Meanwhile, the SAM selectively aggregates the intra-frame features within one instance for spatially consecutive predictions. Finally, we sum the outputs of the two attention modules to further improve feature representations which contribute to more precise segmentation predictions across temporal and spatial dimensions simultaneously. Extensive experiments demonstrate the effectiveness of the proposed method, obtaining state-of-the-art mean intersection of union (mIoU) of 75.22 on CamVid dataset.
关键词	Semantics Image segmentation Feature extraction Active appearance model Optical imaging Context modeling Task analysis Semantic video segmentation attention convolutional neural networks
DOI	10.1109/ACCESS.2019.2943365
关键词[WOS]	DEEP ; DECODER
收录类别	SCI
语种	英语
资助项目	National Nature Science Foundation of China[61671054] ; Beijing Natural Science Foundation[4182038] ; National Nature Science Foundation of China[61671054] ; Beijing Natural Science Foundation[4182038]
项目资助者	National Nature Science Foundation of China ; Beijing Natural Science Foundation
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000497156000044
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/29338
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Li, Jiangyun
作者单位	1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 2.Minist Educ, Key Lab Knowledge Automat Ind Proc, Beijing 100083, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 102488, Peoples R China
推荐引用方式 GB/T 7714	Li, Jiangyun,Zhao, Yikai,Fu, Jun,et al. Attention-Guided Network for Semantic Video Segmentation[J]. IEEE ACCESS,2019,7:140680-140689.
APA	Li, Jiangyun,Zhao, Yikai,Fu, Jun,Wu, Jiajia,&Liu, Jing.(2019).Attention-Guided Network for Semantic Video Segmentation.IEEE ACCESS,7,140680-140689.
MLA	Li, Jiangyun,et al."Attention-Guided Network for Semantic Video Segmentation".IEEE ACCESS 7(2019):140680-140689.