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Gated Object-Attribute Matching Network for Detailed Image Caption
Joint Authors
Xu, Zhiwei
Yun, Jing
Gao, GuangLai
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-13
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Image caption enables computers to generate a text description of images automatically.
However, the generated description is not good enough recently.
Computers can describe what objects are in the image but cannot give more details about these objects.
In this study, we present a novel image caption approach to give more details when describing objects.
In detail, a visual attention-based LSTM is used to find the objects, as well as a semantic attention-based LSTM is used for giving semantic attributes.
At last, a gated object-attribute matching network is used to match the objects to their semantic attributes.
The experiments on the public datasets of Flickr30k and MSCOCO demonstrate that the proposed approach improved the quality of the image caption, compared with the most advanced methods at present.
American Psychological Association (APA)
Yun, Jing& Xu, Zhiwei& Gao, GuangLai. 2020. Gated Object-Attribute Matching Network for Detailed Image Caption. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1202332
Modern Language Association (MLA)
Yun, Jing…[et al.]. Gated Object-Attribute Matching Network for Detailed Image Caption. Mathematical Problems in Engineering No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1202332
American Medical Association (AMA)
Yun, Jing& Xu, Zhiwei& Gao, GuangLai. Gated Object-Attribute Matching Network for Detailed Image Caption. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1202332
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202332