Kinematic skeleton graph augmented network for human parsing

doi:10.1016/j.neucom.2020.07.002

CASIA OpenIR > 智能感知与计算研究中心

	Kinematic skeleton graph augmented network for human parsing
	Liu, Jinde 1,2; Zhang, Zhang1,2 ; Shan, Caifeng 3,4; Tan, Tieniu1,2
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-11-06
卷号	413 页码:457-470
通讯作者	Zhang, Zhang(zzhang@nlpr.ia.ac.cn)
摘要	Human parsing, which is a task of labeling pixels in human images into different fine-grained semantic parts, has achieved significant progress during the past decade. However, there are still several challenges in human parsing, due to occlusions, varying poses and similar appearance between the left/right parts. To tackle these problems, a Human Kinematic Skeleton Graph Layer (HKSGL) is proposed to augment regular neural networks with human kinematic skeleton information. The HKSGL has two major components: kinematic skeleton graph and interconnected modular neural layer. The kinematic skeleton graph is a user pre-defined skeleton graph, which models the interconnections between different semantic parts. Then the skeleton graph is passed to the interconnected modular neural layer which is composed of a set of modular blocks corresponding to different semantic parts. The HKSGL is a lightweight, low costs layer which can be easily attached to any existing neural networks. To demonstrate the power of the HKSGL, a new dataset on human parsing in occlusions is also collected, termed the RAP-Occ. Extensive experiments have been performed on four datasets on human parsing, including the LIP, the CIHP, the ATR and the RAP-Occ. And two popular baselines, i.e., the Deeplab V3+ and the CE2P, are agumented by the proposed HKSGL. Competitive performance of the augmented models has been achieved in comparison with state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.
关键词	Image segmentation Human parsing Deeplab V3+ Kinematic skeleton graph Human parsing dataset
DOI	10.1016/j.neucom.2020.07.002
关键词[WOS]	SEGMENTATION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB1001002] ; Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project)[2019JZZY010119]
项目资助者	National Key Research and Development Program of China ; Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000579803700037
出版者	ELSEVIER
七大方向——子方向分类	类脑模型与计算
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/42126
专题	智能感知与计算研究中心
通讯作者	Zhang, Zhang
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Beijing, Peoples R China 3.CAS CAS AIR, Artificial Intelligence Res, Beijing, Peoples R China 4.Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Liu, Jinde,Zhang, Zhang,Shan, Caifeng,et al. Kinematic skeleton graph augmented network for human parsing[J]. NEUROCOMPUTING,2020,413:457-470.
APA	Liu, Jinde,Zhang, Zhang,Shan, Caifeng,&Tan, Tieniu.(2020).Kinematic skeleton graph augmented network for human parsing.NEUROCOMPUTING,413,457-470.
MLA	Liu, Jinde,et al."Kinematic skeleton graph augmented network for human parsing".NEUROCOMPUTING 413(2020):457-470.