Syntax-Guided Hierarchical Attention Network for Video Captioning

doi:10.1109/TCSVT.2021.3063423

	Syntax-Guided Hierarchical Attention Network for Video Captioning
	Deng, Jincan 1,2; Li, Liang 1,2; Zhang, Beichen 3,4; Wang, Shuhui 1,2; Zha, Zhengjun 5; Huang, Qingming 1,2,3,4
	2022-02-01
发表期刊	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
ISSN	1051-8215
卷号	32 期号:2 页码:880-892
摘要	Video captioning is a challenging task that aims to generate linguistic description based on video content. Most methods only incorporate visual features (2D/3D) as input for generating visual and non-visual words in the caption. However, generating non-visual words usually depends more on sentence-context than visual features. The wrong non-visual words can reduce the sentence fluency and even change the meaning of sentence. In this paper, we propose a syntax-guided hierarchical attention network (SHAN), which leverages semantic and syntax cues to integrate visual and sentence-context features for captioning. First, a globally-dependent context encoder is designed to extract the global sentence-context feature that facilitates generating non-visual words. Then, we introduce hierarchical content attention and syntax attention to adaptively integrate features in terms of temporality and feature characteristics respectively. Content attention helps focus on time intervals related to the semantic of current word, while cross-modal syntax attention uses syntax information to model importance of different features for target word's generation. Moreover, such hierarchical attention can enhance the model interpretability for captioning. Experiments on MSVD and MSR-VTT datasets show the comparable performance of our method compared with current methods.
关键词	Syntactics Feature extraction Visualization Generators Semantics Two dimensional displays Three-dimensional displays Video captioning syntax attention content attention global sentence-context
DOI	10.1109/TCSVT.2021.3063423
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1300201] ; National Natural Science Foundation of China[61771457] ; National Natural Science Foundation of China[61732007] ; National Natural Science Foundation of China[61672497] ; National Natural Science Foundation of China[U19B2038] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[61772494] ; National Natural Science Foundation of China[62022083]
WOS研究方向	Engineering
WOS类目	Engineering, Electrical & Electronic
WOS记录号	WOS:000752017700036
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：38[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19005
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Li, Liang
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, CAS, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China 4.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China 5.Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Peoples R China
推荐引用方式 GB/T 7714	Deng, Jincan,Li, Liang,Zhang, Beichen,et al. Syntax-Guided Hierarchical Attention Network for Video Captioning[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(2):880-892.
APA	Deng, Jincan,Li, Liang,Zhang, Beichen,Wang, Shuhui,Zha, Zhengjun,&Huang, Qingming.(2022).Syntax-Guided Hierarchical Attention Network for Video Captioning.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(2),880-892.
MLA	Deng, Jincan,et al."Syntax-Guided Hierarchical Attention Network for Video Captioning".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.2(2022):880-892.