![](/images/graphics-bg.png)
An Overview of Image Caption Generation Methods
Joint Authors
Yu, Xiaosheng
Wang, Haoran
Zhang, Yue
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-09
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task.
Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing.
The application of image caption is extensive and significant, for example, the realization of human-computer interaction.
This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks.
Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field.
Finally, this paper highlights some open challenges in the image caption task.
American Psychological Association (APA)
Wang, Haoran& Zhang, Yue& Yu, Xiaosheng. 2020. An Overview of Image Caption Generation Methods. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1138738
Modern Language Association (MLA)
Wang, Haoran…[et al.]. An Overview of Image Caption Generation Methods. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1138738
American Medical Association (AMA)
Wang, Haoran& Zhang, Yue& Yu, Xiaosheng. An Overview of Image Caption Generation Methods. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1138738
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138738