模式
计算机科学
模态(人机交互)
人工智能
机器学习
系统回顾
领域(数学分析)
多学科方法
多模式学习
代表(政治)
机器翻译
数据科学
梅德林
数学分析
社会学
政治
社会科学
法学
数学
政治学
作者
Arnab Barua,Mobyen Uddin Ahmed,Shahina Begum
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:11: 14804-14831
被引量:35
标识
DOI:10.1109/access.2023.3243854
摘要
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 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) 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.
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