Syntax-Guided Hierarchical Attention Network for Video Captioning
文献类型：期刊论文
| 作者 | Deng, Jincan3,4; Li, Liang3,4; Zhang, Beichen1,2; Wang, Shuhui3,4; Zha, Zhengjun5; Huang, Qingming1,2,3,4 |
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| 出版日期 | 2022-02-01 |
| 卷号 | 32期号:2页码:880-892 |
| 关键词 | Syntactics Feature extraction Visualization Generators Semantics Two dimensional displays Three-dimensional displays Video captioning syntax attention content attention global sentence-context |
| ISSN号 | 1051-8215 |
| DOI | 10.1109/TCSVT.2021.3063423 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000752017700036 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19004]  |
| 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.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 101408, Peoples R China 2.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, CAS, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, CAS, Beijing 100190, 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. |
入库方式： OAI收割
来源：计算技术研究所
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