Attention-Guided Network for Semantic Video Segmentation | |
Li, Jiangyun1,4; Zhao, Yikai1,4; Fu, Jun2; Wu, Jiajia3; Liu, Jing2 | |
刊名 | IEEE ACCESS
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2019 | |
卷号 | 7页码:140680-140689 |
关键词 | Semantics Image segmentation Feature extraction Active appearance model Optical imaging Context modeling Task analysis Semantic video segmentation attention convolutional neural networks |
ISSN号 | 2169-3536 |
DOI | 10.1109/ACCESS.2019.2943365 |
通讯作者 | Li, Jiangyun(leejy@ustb.edu.cn) |
英文摘要 | 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. |
资助项目 | National Nature Science Foundation of China[61671054] ; Beijing Natural Science Foundation[4182038] |
WOS关键词 | DEEP ; DECODER |
WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000497156000044 |
资助机构 | National Nature Science Foundation of China ; Beijing Natural Science Foundation |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/29337] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
通讯作者 | Li, Jiangyun |
作者单位 | 1.Minist Educ, Key Lab Knowledge Automat Ind Proc, Beijing 100083, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 102488, Peoples R China 4.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, 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. |
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