Combinatorial Analysis of Deep Learning and Machine Learning Video Captioning Studies: A Systematic Literature Review Article Swipe
Tanzila Kehkashan
,
Abdullah Alsaeedi
,
Wael M. S. Yafooz
,
Nor Azman Ismail
,
Arafat Al-Dhaqm
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3357980
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3357980
Recent improvements formulated in the area of video captioning have brought rapid revolutions in its methods and the performance of its models. Machine learning and deep learning techniques are both employed in this regard. However, there is a lack of tracing the latest studies and their remarkable results. Although several studies have been proposed employing the ML and DL algorithms in different other areas, there is no systematic review utilizing the video captioning task. This study aims to examine, evaluate, and synthesize the primary studies into a thorough Systematic Literature Review (SLR) that provides a general overview of the methods used for video captioning. We performed the SLR to determine the research problems under which machine learning models were preferred over the deep learning models and vice versa. We collected a total of 1,656 studies retrieved from four electronic databases; Scopus, WoS, IEEE Xplore, and ACM, based on our search string from which 162 published studies passed the selection criteria related to one primary and two secondary research questions after a systematic process. Moreover, insufficient data collection and inefficient comparison of results are common issues identified during the review process. We conclude that the 2D/3D CNN for video feature extraction and LSTM for caption generation, METEOR and BLEU performance evaluation tools, and MSVD dataset are most frequently employed for video captioning. Our study is the pioneer in comparing the implementation of ML and DL algorithms employing the video captioning area. Thus, our study will accelerate the critical assessment of the state-of-the-art in other research fields of video analysis and human-computer interaction.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3357980
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10413461.pdf
- OA Status
- gold
- Cited By
- 6
- References
- 195
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391164068
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391164068Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3357980Digital Object Identifier
- Title
-
Combinatorial Analysis of Deep Learning and Machine Learning Video Captioning Studies: A Systematic Literature ReviewWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Tanzila Kehkashan, Abdullah Alsaeedi, Wael M. S. Yafooz, Nor Azman Ismail, Arafat Al-DhaqmList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2024.3357980Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10413461.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10413461.pdfDirect OA link when available
- Concepts
-
Closed captioning, Computer science, Artificial intelligence, Machine learning, Deep learning, Natural language processing, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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195Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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