Image captioning with triple-attention and stack parallel LSTM

doi:10.1016/j.neucom.2018.08.069

	Image captioning with triple-attention and stack parallel LSTM
	Zhu, Xinxin 2,3; Li, Lixiang 2,3; Liu, Jing1 ; Li, Ziyi 4; Peng, Haipeng 2,3; Niu, Xinxin 2,3
刊名	NEUROCOMPUTING
	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
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000446229200006
资助机构	National Key R&D Program of China ; National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/28108]
专题	中国科学院自动化研究所
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.