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CGNet: A Light-Weight Context Guided Network for Semantic Segmentation

文献类型：期刊论文

作者Wu, Tianyi1,2; Tang, Sheng1,2; Zhang, Rui1,2; Cao, Juan1,2; Zhang, Yongdong1,2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2021
卷号30页码:1169-1179
ISSN号1057-7149
关键词Semantics Image segmentation Context modeling Computer architecture Computational modeling Mobile handsets Predictive models Semantic segmentation surrounding context global context context guided
DOI10.1109/TIP.2020.3042065
英文摘要The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other small memory footprint models follow the spirit of classification network and ignore the inherent characteristic of semantic segmentation. To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight and efficient network for semantic segmentation. We first propose the Context Guided (CG) block, which learns the joint feature of both local feature and surrounding context effectively and efficiently, and further improves the joint feature with the global context. Based on the CG block, we develop CGNet which captures contextual information in all stages of the network. CGNet is specially tailored to exploit the inherent property of semantic segmentation and increase the segmentation accuracy. Moreover, CGNet is elaborately designed to reduce the number of parameters and save memory footprint. Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing light-weight segmentation networks. Extensive experiments on Cityscapes and CamVid datasets verify the effectiveness of the proposed approach. Specifically, without any post-processing and multi-scale testing, the proposed CGNet achieves 64.8% mean IoU on Cityscapes with less than 0.5 M parameters.
资助项目National Key Research and Development Program of China[2017YFC0820605] ; National Natural Science Foundation of China[61525206] ; National Natural Science Foundation of China[U1703261] ; National Natural Science Foundation of China[61871004]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000600835900003
源URL[http://119.78.100.204/handle/2XEOYT63/16583]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tang, Sheng
作者单位1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Tianyi,Tang, Sheng,Zhang, Rui,et al. CGNet: A Light-Weight Context Guided Network for Semantic Segmentation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2021,30:1169-1179.
APA Wu, Tianyi,Tang, Sheng,Zhang, Rui,Cao, Juan,&Zhang, Yongdong.(2021).CGNet: A Light-Weight Context Guided Network for Semantic Segmentation.IEEE TRANSACTIONS ON IMAGE PROCESSING,30,1169-1179.
MLA Wu, Tianyi,et al."CGNet: A Light-Weight Context Guided Network for Semantic Segmentation".IEEE TRANSACTIONS ON IMAGE PROCESSING 30(2021):1169-1179.

入库方式： OAI收割

来源：计算技术研究所

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