CASIA OpenIR
Image captioning with triple-attention and stack parallel LSTM
Zhu, Xinxin1,2; Li, Lixiang1,2; Liu, Jing3; Li, Ziyi4; Peng, Haipeng1,2; Niu, Xinxin1,2
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2018-11-30
Volume319Pages:55-65
Corresponding AuthorLi, Lixiang(li_lixiang2006@163.com)
AbstractImage 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.
KeywordImage caption Deep learning LSTM CNN Attention
DOI10.1016/j.neucom.2018.08.069
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China[2016YFB0800602] ; National Natural Science Foundation of China[61573067] ; National Natural Science Foundation of China[61771071]
Funding OrganizationNational Key R&D Program of China ; National Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000446229200006
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28109
Collection中国科学院自动化研究所
Corresponding AuthorLi, Lixiang
Affiliation1.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China
2.Beijing Univ Posts & Telecommun, Natl Engn Lab Disaster Backup & Recovery, Beijing 100876, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
4.Beijing Technol & Business Univ, Beijing 100048, Peoples R China
Recommended Citation
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.
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