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Super-resolution semantic segmentation with relation calibrating network

文献类型：期刊论文

作者Jiang, Jie1,2; Liu, Jing1,2; Fu, Jun2; Wang, Weining1,2; Lu, Hanqing1,2
刊名PATTERN RECOGNITION
出版日期2022-04-01
卷号124页码:10
关键词Image semantic segmentation Super-resolution semantic segmentation Relation calibrating
ISSN号0031-3203
DOI10.1016/j.patcog.2021.108501
通讯作者Liu, Jing(jliu@nlpr.ia.ac.cn)
英文摘要To achieve high-resolution segmentation results, typical semantic segmentation models often require high-resolution inputs. However, high-resolution inputs inevitably bring high cost on computation, which limits its application seriously in realistic scenarios. To address the problem, we propose to predict a high-resolution semantic segmentation result with a degraded low-resolution image as input, which is called super-resolution semantic segmentation in this paper. We further propose a Relation Calibrating Network (RCNet) for this task. Specifically, we propose two modules, namely Relation Upsampling Module (RUM) and Feature Calibrating Module (FCM). In RUM, the input feature map generates the relation map of pixels in low-resolution, which is then gradually upsampled to high-resolution. Meanwhile, FCM takes the input feature map and the relation map from RUM as inputs, gradually calibrating the feature. Finally, the last FCM outputs the high-resolution segmentation results. We conduct extensive experiments to verify the effectiveness of our method. Specially, we achieve a comparable segmentation result (from 70.01% to 70.90%) with only 1/4 of the computational cost (from 1107.57 to 255.72 GFLOPs) based on FCN on Cityscapes dataset. (c) 2021 Elsevier Ltd. All rights reserved.
资助项目National Natural Science Foundation of China[61922086] ; National Natural Science Foundation of China[61872366] ; Beijing Natural Science Foundation[4192059] ; Beijing Natural Science Foundation[JQ20022]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000740812500002
出版者ELSEVIER SCI LTD
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/47178]  
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Liu, Jing
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Jie,Liu, Jing,Fu, Jun,et al. Super-resolution semantic segmentation with relation calibrating network[J]. PATTERN RECOGNITION,2022,124:10.
APA Jiang, Jie,Liu, Jing,Fu, Jun,Wang, Weining,&Lu, Hanqing.(2022).Super-resolution semantic segmentation with relation calibrating network.PATTERN RECOGNITION,124,10.
MLA Jiang, Jie,et al."Super-resolution semantic segmentation with relation calibrating network".PATTERN RECOGNITION 124(2022):10.

入库方式： OAI收割

来源：自动化研究所

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