A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions
Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. Howe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10041115/ |
| _version_ | 1797904592692838400 |
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| author | Arnab Barua Mobyen Uddin Ahmed Shahina Begum |
| author_facet | Arnab Barua Mobyen Uddin Ahmed Shahina Begum |
| author_sort | Arnab Barua |
| collection | DOAJ |
| description | Multimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role in tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) was applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions. |
| first_indexed | 2024-04-10T09:52:30Z |
| format | Article |
| id | doaj.art-d13bb5a652504f2f872a503487b4450d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:52:30Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d13bb5a652504f2f872a503487b4450d2023-02-17T00:00:22ZengIEEEIEEE Access2169-35362023-01-0111148041483110.1109/ACCESS.2023.324385410041115A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future DirectionsArnab Barua0https://orcid.org/0000-0002-9698-8142Mobyen Uddin Ahmed1https://orcid.org/0000-0003-1953-6086Shahina Begum2School of Innovation, Design and Engineering, Mälardalen University, Västerås, SwedenSchool of Innovation, Design and Engineering, Mälardalen University, Västerås, SwedenSchool of Innovation, Design and Engineering, Mälardalen University, Västerås, SwedenMultimodal machine learning (MML) is a tempting multidisciplinary research area where heterogeneous data from multiple modalities and machine learning (ML) are combined to solve critical problems. Usually, research works use data from a single modality, such as images, audio, text, and signals. However, real-world issues have become critical now, and handling them using multiple modalities of data instead of a single modality can significantly impact finding solutions. ML algorithms play an essential role in tuning parameters in developing MML models. This paper reviews recent advancements in the challenges of MML, namely: representation, translation, alignment, fusion and co-learning, and presents the gaps and challenges. A systematic literature review (SLR) was applied to define the progress and trends on those challenges in the MML domain. In total, 1032 articles were examined in this review to extract features like source, domain, application, modality, etc. This research article will help researchers understand the constant state of MML and navigate the selection of future research directions.https://ieeexplore.ieee.org/document/10041115/Multimodal machine learningsystematic literature reviewrepresentationtranslationalignmentfusion |
| spellingShingle | Arnab Barua Mobyen Uddin Ahmed Shahina Begum A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions IEEE Access Multimodal machine learning systematic literature review representation translation alignment fusion |
| title | A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions |
| title_full | A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions |
| title_fullStr | A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions |
| title_full_unstemmed | A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions |
| title_short | A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions |
| title_sort | systematic literature review on multimodal machine learning applications challenges gaps and future directions |
| topic | Multimodal machine learning systematic literature review representation translation alignment fusion |
| url | https://ieeexplore.ieee.org/document/10041115/ |
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