Vol. 3 No. 1 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Deep Learning Approaches for Automated Image Captioning: A Comparative Analysis

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  • Dr. Daniel Koppelman
Dr. Daniel Koppelman
Professor of Computer Science, University of Haifa, Israel
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Published 30-06-2023

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Daniel Koppelman, “Deep Learning Approaches for Automated Image Captioning: A Comparative Analysis”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 1, pp. 248–261, Jun. 2023, Accessed: Nov. 21, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/86

Abstract

In 2014, deep learning-based end-to-end image captioning models were brought into play for the very first time, and since then, researchers from several domains have demonstrated their deep learning capabilities by proposing various architectures. These innovative ideas tend towards the reduction in model size and parameters along with improved performance. A plethora of methods introduced deep learning models as the state-of-the-art resulting from enhancements through leveraging different properties of the spatial regions and temporal sequences. These properties suggest a wide span from spatial attention mechanisms to temporal extension. Despite these state-of-the-art models, a comprehensive evaluation from a performance perspective has not been presented.

Image captioning is the process of creating a textual description of a given image. It combines visual recognition and natural language processing. Recently, deep learning methods have been introduced to enable the automatic generation of human-like captions. In this paper, a comprehensive survey of deep learning-based image captioning models is presented, covering the early approaches to the state-of-the-art models. A performance-centric in-depth comparative analysis of these models is presented. It encompasses a qualitative and quantitative comparison of various models in terms of spatial and temporal attention mechanisms, different encoding and decoding architectures, extensions of RNN, and the use of extra information like external language models. Finally, the findings of the comparative analysis are summarized with the associated challenges and trends of future research indicated. Moreover, methods to tackle the common issues of generated captions and a discussion on evaluation metrics are also presented, respectively.

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