Knowledge Commons of Institute of Automation,CAS
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 | |
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. |
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