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Property-Constrained Dual Learning for Video Summarization
Zhao, Bin1,2; Li, Xuelong1,2; Lu, Xiaoqiang3
作者部门光谱成像技术研究室
2020-10
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X;2162-2388
卷号31期号:10页码:3989-4000
产权排序3
摘要

Video summarization is the technique to condense large-scale videos into summaries composed of key-frames or key-shots so that the viewers can browse the video content efficiently. Recently, supervised approaches have achieved great success by taking advantages of recurrent neural networks (RNNs). Most of them focus on generating summaries by maximizing the overlap between the generated summary and the ground truth. However, they neglect the most critical principle, i.e., whether the viewer can infer the original video content from the summary. As a result, existing approaches cannot preserve the summary quality well and usually demand large amounts of training data to reduce overfitting. In our view, video summarization has two tasks, i.e., generating summaries from videos and inferring the original content from summaries. Motivated by this, we propose a dual learning framework by integrating the summary generation (primal task) and video reconstruction (dual task) together, which targets to reward the summary generator under the assistance of the video reconstructor. Moreover, to provide more guidance to the summary generator, two property models are developed to measure the representativeness and diversity of the generated summary. Practically, experiments on four popular data sets (SumMe, TVsum, OVP, and YouTube) have demonstrated that our approach, with compact RNNs as the summary generator, using less training data, and even in the unsupervised setting, can get comparable performance with those supervised ones adopting more complex summary generators and trained on more annotated data.

关键词Generators Task analysis Recurrent neural networks Machine learning Training Semantics Learning systems Dual learning property model recurrent neural network (RNN) video summarization
DOI10.1109/TNNLS.2019.2951680
收录类别SCI ; EI
语种英语
WOS记录号WOS:000576436600017
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号20204509445393
引用统计
被引频次:41[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.opt.ac.cn/handle/181661/93737
专题光谱成像技术研究室
通讯作者Lu, Xiaoqiang
作者单位1.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China
2.Northwestern Polytech Univ, Ctr OPT Imagery Anal & Learning OPTIMAL, Xian 710072, Peoples R China
3.Chinese Acad Sci, Key Lab Spectral Imaging Technol CAS, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
推荐引用方式
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Zhao, Bin,Li, Xuelong,Lu, Xiaoqiang. Property-Constrained Dual Learning for Video Summarization[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(10):3989-4000.
APA Zhao, Bin,Li, Xuelong,&Lu, Xiaoqiang.(2020).Property-Constrained Dual Learning for Video Summarization.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(10),3989-4000.
MLA Zhao, Bin,et al."Property-Constrained Dual Learning for Video Summarization".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.10(2020):3989-4000.
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