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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

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Journal article

Venue:

Journal of IEEE Access

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. Usu- ally, 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 by 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

Bibtex

@article{Barua6628,
author = {Arnab Barua and Mobyen Uddin Ahmed and Shahina Begum},
title = {A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions},
volume = {2},
number = {1},
pages = {1--50},
month = {March},
year = {2023},
journal = {Journal of IEEE Access},
url = {http://www.es.mdu.se/publications/6628-}
}