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A Systematic Literature Review on Image Captioning

Department of Information Technologies, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT-10223 Vilnius, Lithuania
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Appl. Sci. 2019, 9(10), 2024; https://doi.org/10.3390/app9102024
Received: 26 March 2019 / Revised: 8 May 2019 / Accepted: 9 May 2019 / Published: 16 May 2019
(This article belongs to the Section Computing and Artificial Intelligence)
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Abstract

Natural language problems have already been investigated for around five years. Recent progress in artificial intelligence (AI) has greatly improved the performance of models. However, the results are still not sufficiently satisfying. Machines cannot imitate human brains and the way they communicate, so it remains an ongoing task. Due to the increasing amount of information on this topic, it is very difficult to keep on track with the newest researches and results achieved in the image captioning field. In this study a comprehensive Systematic Literature Review (SLR) provides a brief overview of improvements in image captioning over the last four years. The main focus of the paper is to explain the most common techniques and the biggest challenges in image captioning and to summarize the results from the newest papers. Inconsistent comparison of results achieved in image captioning was noticed during this study and hence the awareness of incomplete data collection is raised in this paper. Therefore, it is very important to compare results of a newly created model produced with the newest information and not only with the state of the art methods. This SLR is a source of such information for researchers in order for them to be precisely correct on result comparison before publishing new achievements in the image caption generation field. View Full-Text
Keywords: image caption generation; NLP; LSTM; semantics; systematic literature review image caption generation; NLP; LSTM; semantics; systematic literature review
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Staniūtė, R.; Šešok, D. A Systematic Literature Review on Image Captioning. Appl. Sci. 2019, 9, 2024.

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