Super-resolution semantic segmentation with relation calibrating network

doi:10.1016/j.patcog.2021.108501

	Super-resolution semantic segmentation with relation calibrating network
	Jiang, Jie1,2 ; Liu, Jing1,2 ; Fu, Jun1 ; Wang, Weining1,2 ; Lu, Hanqing1,2
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2022-04-01
卷号	124 页码:10
通讯作者	Liu, Jing(jliu@nlpr.ia.ac.cn)
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.
关键词	Image semantic segmentation Super-resolution semantic segmentation Relation calibrating
DOI	10.1016/j.patcog.2021.108501
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000740812500002
出版者	ELSEVIER SCI LTD
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：5[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/173211/47179
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Liu, Jing
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
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.