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	Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation
	Yang, ZG; Yu, HS; Sun, W; Mao, ZH; Sun, MG
刊名	IEEE ACCESS
	2019
卷号	Vol.7页码:2263-2272
关键词	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/4738936
专题	湖南大学
作者单位	1.Hunan Univ, Coll Elect & Informat Engn, Natl Engn Lab Robot Visual Percept & Control Tech, Changsha 410082, Hunan, Peoples R China 2.Univ Pittsburgh, Lab Computat Neurosci, Pittsburgh, PA 15260 USA 3.Univ Pittsburgh, Dept Elect & Comp Engn, Pittsburgh, PA 15260 USA
推荐引用方式 GB/T 7714	Yang, ZG,Yu, HS,Sun, W,et al. Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation[J]. IEEE ACCESS,2019,Vol.7:2263-2272.
APA	Yang, ZG,Yu, HS,Sun, W,Mao, ZH,&Sun, MG.(2019).Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation.IEEE ACCESS,Vol.7,2263-2272.
MLA	Yang, ZG,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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