Image captioning with triple-attention and stack parallel LSTM
文献类型：期刊论文
| 作者 | Zhu, Xinxin2,3 ; Li, Lixiang2,3; Liu, Jing1; Li, Ziyi4; Peng, Haipeng2,3; Niu, Xinxin2,3
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| 刊名 | NEUROCOMPUTING
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| 出版日期 | 2018-11-30 |
| 卷号 | 319页码:55-65 |
| 关键词 | Image caption Deep learning LSTM CNN Attention |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2018.08.069 |
| 通讯作者 | Li, Lixiang(li_lixiang2006@163.com) |
| 英文摘要 | Image captioning aims to describe the content of images with a sentence. It is a natural way for people to express their understanding, but a challenging and important task from the view of image understanding. In this paper, we propose two innovations to improve the performance of such a sequence learning problem. First, we give a new attention method named triple attention (TA-LSTM) which can leverage the image context information at every stage of LSTM. Then, we redesign the structure of basic LSTM, in which not only the stacked LSTM but also the paralleled LSTM are adopted, called as PS-LSTM. In this structure, we not only use the stack LSTM but also use the parallel LSTM to achieve the improvement of the performance compared with the normal LSTM. Through this structure, the proposed model can ensemble more parameters on single model and has ensemble ability itself. Through numerical experiments, on the public available MSCOCO dataset, our final TA-PS-LSTM model achieves comparable performance with some state-of-the-art methods. (c) 2018 Elsevier B.V. All rights reserved. |
| 资助项目 | National Key R&D Program of China[2016YFB0800602] ; National Natural Science Foundation of China[61573067] ; National Natural Science Foundation of China[61771071] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000446229200006 |
| 出版者 | ELSEVIER SCIENCE BV |
| 资助机构 | National Key R&D Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28108]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 通讯作者 | Li, Lixiang |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 2.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China 3.Beijing Univ Posts & Telecommun, Natl Engn Lab Disaster Backup & Recovery, Beijing 100876, Peoples R China 4.Beijing Technol & Business Univ, Beijing 100048, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Xinxin,Li, Lixiang,Liu, Jing,et al. Image captioning with triple-attention and stack parallel LSTM[J]. NEUROCOMPUTING,2018,319:55-65. |
| APA | Zhu, Xinxin,Li, Lixiang,Liu, Jing,Li, Ziyi,Peng, Haipeng,&Niu, Xinxin.(2018).Image captioning with triple-attention and stack parallel LSTM.NEUROCOMPUTING,319,55-65. |
| MLA | Zhu, Xinxin,et al."Image captioning with triple-attention and stack parallel LSTM".NEUROCOMPUTING 319(2018):55-65. |
入库方式： OAI收割
来源：自动化研究所
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