Text2Video: An End-to-end Learning Framework for Expressing Text With Videos

doi:10.1109/TMM.2018.2807588

	Text2Video: An End-to-end Learning Framework for Expressing Text With Videos
	Yang, Xiaoshan1,2 ; Zhang, Tianzhu1,2 ; Xu, Changsheng1,2
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
	2018-09-01
卷号	20 期号:9 页码:2360-2370
文章类型	Article
摘要	Video creation is a challenging and highly professional task that generally involves substantial manual efforts. To ease this burden, a better approach is to automatically produce new videos based on clips from the massive amount of existing videos according to arbitrary text. In this paper, we formulate video creation as a problem of retrieving a sequence of videos for a sentence stream. To achieve this goal, we propose a novel multimodal recurrent architecture for automatic video production. Compared with existing methods, the proposed model has three major advantages. First, it is the first completely integrated end-to-end deep learning system for real-world production to the best of our knowledge. We are among the first to address the problem of retrieving a sequence of videos for a sentence stream. Second, it can effectively exploit the correspondence between sentences and video clips through semantic consistency modeling. Third, it can model the visual coherence well by requiring that the produced videos should be organized coherently in terms of visual appearance. We have conducted extensive experiments on two applications, including video retrieval and video composition. The qualitative and quantitative results obtained on two public datasets used in the Large Scale Movie Description Challenge 2016 both demonstrate the effectiveness of the proposed model compared with other state-of-the-art algorithms.
关键词	Multimedia Storytelling Video Analysis Deep Learning
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TMM.2018.2807588
关键词[WOS]	ANNOTATION ; REPRESENTATION ; NARRATIVES ; MOVIE ; WEB ; TV
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61432019 ; Beijing Natural Science Foundation(4172062) ; Key Research Program of Frontier Sciences, CAS(QYZDJ-SSW-JSC039) ; 61572498 ; 61532009 ; 61702511 ; 61720106006 ; 61711530243)
WOS研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000442358200010
引用统计	被引频次：13[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/173211/20467
专题	多模态人工智能系统全国重点实验室_多媒体计算
作者单位	1.National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yang, Xiaoshan,Zhang, Tianzhu,Xu, Changsheng. Text2Video: An End-to-end Learning Framework for Expressing Text With Videos[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2018,20(9):2360-2370.
APA	Yang, Xiaoshan,Zhang, Tianzhu,&Xu, Changsheng.(2018).Text2Video: An End-to-end Learning Framework for Expressing Text With Videos.IEEE TRANSACTIONS ON MULTIMEDIA,20(9),2360-2370.
MLA	Yang, Xiaoshan,et al."Text2Video: An End-to-end Learning Framework for Expressing Text With Videos".IEEE TRANSACTIONS ON MULTIMEDIA 20.9(2018):2360-2370.

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