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

Biology

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