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A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions

Authors :
Arnab Barua
Mobyen Uddin Ahmed
Shahina Begum
Source :
IEEE Access, Vol 11, Pp 14804-14831 (2023)
Publication Year :
2023
Publisher :
IEEE, 2023.

Abstract

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.

Details

Language :
English
ISSN :
21693536
Volume :
11
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.13bb5a652504f2f872a503487b4450d
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2023.3243854