From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning | |
Song, Jingkuan1; Guo, Yuyu1; Gao, Lianli1; Li, Xuelong2; Hanjalic, Alan3; Shen, Heng Tao1 | |
作者部门 | 光谱成像技术研究室 |
2019-10 | |
发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS |
ISSN | 2162-237X;2162-2388 |
卷号 | 30期号:10页码:3047-3058 |
产权排序 | 2 |
摘要 | Video captioning, in essential, is a complex natural process, which is affected by various uncertainties stemming from video content, subjective judgment, and so on. In this paper, we build on the recent progress in using encoder-decoder framework for video captioning and address what we find to be a critical deficiency of the existing methods that most of the decoders propagate deterministic hidden states. Such complex uncertainty cannot be modeled efficiently by the deterministic models. In this paper, we propose a generative approach, referred to as multimodal stochastic recurrent neural networks (MS-RNNs), which models the uncertainty observed in the data using latent stochastic variables. Therefore, MS-RNN can improve the performance of video captioning and generate multiple sentences to describe a video considering different random factors. Specifically, a multimodal long short-term memory (LSTM) is first proposed to interact with both visual and textual features to capture a high-level representation. Then, a backward stochastic LSTM is proposed to support uncertainty propagation by introducing latent variables. Experimental results on the challenging data sets, microsoft video description and microsoft research videoto-text, show that our proposed MS-RNN approach outperforms the state-of-the-art video captioning benchmarks. |
关键词 | Recurrent neural network (RNN) uncertainty video captioning |
DOI | 10.1109/TNNLS.2018.2851077 |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000487199000014 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/31880 |
专题 | 光谱成像技术研究室 |
作者单位 | 1.Univ Elect Sci & Technol China, Ctr Future Media, Chengdu 611731, Sichuan, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China 3.Delft Univ Technol, Dept Intelligent Syst, Delft, Netherlands |
推荐引用方式 GB/T 7714 | Song, Jingkuan,Guo, Yuyu,Gao, Lianli,et al. From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2019,30(10):3047-3058. |
APA | Song, Jingkuan,Guo, Yuyu,Gao, Lianli,Li, Xuelong,Hanjalic, Alan,&Shen, Heng Tao.(2019).From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,30(10),3047-3058. |
MLA | Song, Jingkuan,et al."From Deterministic to Generative: Multimodal Stochastic RNNs for Video Captioning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 30.10(2019):3047-3058. |
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