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Semi-global shape-aware attention network for image segmentation and retrieval

文献类型：期刊论文

作者Zhang, Pengju3,4; Zhu, Jiagang1; Zhang, Chaofan2; Rong, Zheng4; Wu, Yihong3,4
刊名NEUROCOMPUTING
出版日期2022-09-28
卷号506页码:369-379
关键词Attention network Shape -awareness Semantic segmentation Image retrieval
ISSN号0925-2312
DOI10.1016/j.neucom.2022.07.069
通讯作者Zhang, Chaofan(zcfan@aiofm.ac.cn) ; Rong, Zheng(zheng.rong@nlpr.ia.ac.cn) ; Wu, Yihong(yhwu@nlpr.ia.ac.cn)
英文摘要Non-local operations are usually used to capture long-range dependencies via aggregating global context to each position recently. However, most of the methods cannot preserve object shapes since they only focus on feature similarity but ignore proximity between central and other positions for capturing long-range dependencies, while shape-awareness is beneficial to many computer vision tasks. In this paper, we propose a Semi-Global Shape-aware Network (SGSNet) considering both feature similarity and prox-imity for preserving object shapes when modeling long-range dependencies. A hierarchical way is taken to aggregate global context. In the first level, each position in the whole feature map only aggregates con-textual information in vertical and horizontal directions according to both similarity and proximity. And then the result is input into the second level to do the same operations. By this hierarchical way, each central position gains supports from all other positions, and the combination of similarity and proximity makes each position gain supports mostly from the same semantic object. Moreover, we also propose an efficient algorithm for the aggregation of contextual information, where each of rows and columns in the feature map is treated as a binary tree to reduce similarity computation cost. Experiments on semantic segmentation and image retrieval show that adding SGSNet to existing networks gains solid improve-ments on both accuracy and efficiency.(c) 2022 Elsevier B.V. All rights reserved.
资助项目National Natural Science Founda- tion of China[61836015] ; National Natural Science Founda- tion of China[62002359] ; National Natural Science Founda- tion of China[62102395]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000843462400012
出版者ELSEVIER
资助机构National Natural Science Founda- tion of China
源URL[http://ir.ia.ac.cn/handle/173211/49891]  
专题自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者Zhang, Chaofan; Rong, Zheng; Wu, Yihong
作者单位1.XForwardAI Technol Co Ltd, Beijing, Peoples R China
2.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Hefei Inst Phys Sci, Anhui 230031, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Pengju,Zhu, Jiagang,Zhang, Chaofan,et al. Semi-global shape-aware attention network for image segmentation and retrieval[J]. NEUROCOMPUTING,2022,506:369-379.
APA Zhang, Pengju,Zhu, Jiagang,Zhang, Chaofan,Rong, Zheng,&Wu, Yihong.(2022).Semi-global shape-aware attention network for image segmentation and retrieval.NEUROCOMPUTING,506,369-379.
MLA Zhang, Pengju,et al."Semi-global shape-aware attention network for image segmentation and retrieval".NEUROCOMPUTING 506(2022):369-379.

入库方式： OAI收割

来源：自动化研究所

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