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	Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation
	Zhengeng Yang; Hongshan Yu; Wei Sun; Zhihong Mao; Mingui Sun
刊名	IEEE Access
	2019
卷号	Vol.7页码:2263-2272
关键词	Semantics Image segmentation Spatial resolution Training Kernel Urban areas Semantic segmentation fully convolutional networks feature learning context exploitation
ISSN号	2169-3536
URL标识	查看原文
公开日期	[db:dc_date_available]
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/4601973
专题	湖南大学
作者单位	1.National Engineering Laboratory for Robot Visual Perception and Control Technology, College of Electrical and Information Engineering, Hunan University, Changsha, China 2.Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA 3.Laboratory for Computational Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
推荐引用方式 GB/T 7714	Zhengeng Yang,Hongshan Yu,Wei Sun,et al. Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation[J]. IEEE Access,2019,Vol.7:2263-2272.
APA	Zhengeng Yang,Hongshan Yu,Wei Sun,Zhihong Mao,&Mingui Sun.(2019).Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation.IEEE Access,Vol.7,2263-2272.
MLA	Zhengeng Yang,et al."Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation".IEEE Access Vol.7(2019):2263-2272.

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