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A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 14804-14831Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2023. Vol. 11, p. 14804-14831
Keywords [en]
alignment, co-learning, fusion, Multimodal machine learning, representation, systematic literature review, translation, Machine learning applications, Machine-learning, Multi-disciplinary research, Multi-modal, Multiple modalities, Machine learning
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-62038DOI: 10.1109/ACCESS.2023.3243854ISI: 000936312800001Scopus ID: 2-s2.0-85149020788OAI: oai:DiVA.org:mdh-62038DiVA, id: diva2:1742083
Available from: 2023-03-08 Created: 2023-03-08 Last updated: 2023-03-15Bibliographically approved

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Barua, ArnabAhmed, Mobyen UddinBegum, Shahina

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