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
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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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