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Multimodal graph convolutional networks for high quality content recognition

文献类型：期刊论文

作者Wang, Jinguang1; Hu, Jun1; Qian, Shengsheng2; Fang, Quan2; Xu, Changsheng1,2
刊名NEUROCOMPUTING
出版日期2020-10-28
卷号412页码:42-51
关键词High quality content recognition Graph convolutional networks Positive unlabeled learning
ISSN号0925-2312
DOI10.1016/j.neucom.2020.04.145
通讯作者Qian, Shengsheng(shengsheng.qian@nlpr.ia.ac.cn)
英文摘要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.
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
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000571637700005
出版者ELSEVIER
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/42009]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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