Multimodal graph convolutional networks for high quality content recognition

doi:10.1016/j.neucom.2020.04.145

	Multimodal graph convolutional networks for high quality content recognition
	Wang, Jinguang 1; Hu, Jun 1; Qian, Shengsheng2 ; Fang, Quan2 ; Xu, Changsheng1,2
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-10-28
卷号	412 页码:42-51
通讯作者	Qian, Shengsheng(shengsheng.qian@nlpr.ia.ac.cn)
摘要	With the development of the Internet, more and more creators publish articles on social media. How to automatically filter high quality content from a large number of multimedia articles is one of the core functions of information recommendation, search engine, and other systems. However, existing approaches typically suffer from two limitations: (1) They usually model content as word sequences, which ignores the semantics provided by non-consecutive phrases, long-distance word dependency, and visual information. (2) They rely on a large amount of manually annotated data to train a quality assessment model while users may only provide labels of interest in a single class for a small number of samples in reality. To address these limitations, we propose a Multimodal Graph Convolutional Networks (MGCN) to model the semantic representations in a unified framework for High Quality Content Recognition. Instead of viewing text content as word sequences, we convert them into graphs, which can model non-consecutive phrases and long-distance word dependency for better obtaining the composition of semantics. Besides, visual content is also modeled into the graphs to provide complementary semantics. A well-designed graph convolutional network is proposed to capture the semantic representations based on these graphs. Furthermore, we employ a non-negative risk estimator for high quality content recognition and the loss is back-propagated for model learning. Experiments on real data sets validate the effectiveness of our approach. (c) 2020 Elsevier B.V. All rights reserved.
关键词	High quality content recognition Graph convolutional networks Positive unlabeled learning
DOI	10.1016/j.neucom.2020.04.145
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1002804] ; National Natural Science Foundation of China[61720106006] ; National Natural Science Foundation of China[61802405] ; National Natural Science Foundation of China[61702509] ; National Natural Science Foundation of China[61832002] ; National Natural Science Foundation of China[61936005] ; National Natural Science Foundation of China[61872424] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC039] ; K.C.Wong Education Foundation
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; K.C.Wong Education Foundation
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000571637700005
出版者	ELSEVIER
七大方向——子方向分类	多模态智能
引用统计	被引频次：8[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/173211/42010
专题	多模态人工智能系统全国重点实验室_多媒体计算
通讯作者	Qian, Shengsheng
作者单位	1.Hefei Univ Technol, Hefei, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Wang, Jinguang,Hu, Jun,Qian, Shengsheng,et al. Multimodal graph convolutional networks for high quality content recognition[J]. NEUROCOMPUTING,2020,412:42-51.
APA	Wang, Jinguang,Hu, Jun,Qian, Shengsheng,Fang, Quan,&Xu, Changsheng.(2020).Multimodal graph convolutional networks for high quality content recognition.NEUROCOMPUTING,412,42-51.
MLA	Wang, Jinguang,et al."Multimodal graph convolutional networks for high quality content recognition".NEUROCOMPUTING 412(2020):42-51.