OPT OpenIR  > 光学影像学习与分析中心
3G structure for image caption generation
Yuan, Aihong1,2; Li, Xuelong1; Lu, Xiaoqiang1
Department光学影像学习与分析中心
2019-02-22
Source PublicationNeurocomputing
ISSN9252312;18728286
Volume330Pages:17-28
Contribution Rank1
AbstractIt is a big challenge of computer vision to make machine automatically describe the content of an image with a natural language sentence. Previous works have made great progress on this task, but they only use the global or local image feature, which may lose some important subtle or global information of an image. In this paper, we propose a model with 3-gated model which fuses the global and local image features together for the task of image caption generation. The model mainly has three gated structures. (1) Gate for the global image feature, which can adaptively decide when and how much the global image feature should be imported into the sentence generator. (2) The gated recurrent neural network (RNN) is used as the sentence generator. (3) The gated feedback method for stacking RNN is employed to increase the capability of nonlinearity fitting. More specially, the global and local image features are combined together in this paper, which makes full use of the image information. The global image feature is controlled by the first gate and the local image feature is selected by the attention mechanism. With the latter two gates, the relationship between image and text can be well explored, which improves the performance of the language part as well as the multi-modal embedding part. Experimental results show that our proposed method outperforms the state-of-the-art for image caption generation. ? 2018
DOI10.1016/j.neucom.2018.10.059
Indexed ByEI
Language英语
PublisherElsevier B.V.
EI Accession Number20184506050720
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/31099
Collection光学影像学习与分析中心
Corresponding AuthorLu, Xiaoqiang
Affiliation1.Center for OPTical IMagery Analysis and Learning (OPTIMAL), Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; Shaanxi; 710119, China;
2.University of Chinese Academy of Sciences, Beijing; 100049, China
Recommended Citation
GB/T 7714
Yuan, Aihong,Li, Xuelong,Lu, Xiaoqiang. 3G structure for image caption generation[J]. Neurocomputing,2019,330:17-28.
APA Yuan, Aihong,Li, Xuelong,&Lu, Xiaoqiang.(2019).3G structure for image caption generation.Neurocomputing,330,17-28.
MLA Yuan, Aihong,et al."3G structure for image caption generation".Neurocomputing 330(2019):17-28.
Files in This Item:
File Name/Size DocType Version Access License
3G structure for ima(3091KB)期刊论文出版稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yuan, Aihong]'s Articles
[Li, Xuelong]'s Articles
[Lu, Xiaoqiang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yuan, Aihong]'s Articles
[Li, Xuelong]'s Articles
[Lu, Xiaoqiang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yuan, Aihong]'s Articles
[Li, Xuelong]'s Articles
[Lu, Xiaoqiang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 3G structure for image caption generation.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.