Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

Multi-modal Learning for Fusion of Heterogeneous Data: Investigating multi-modal learning approaches for integrating heterogeneous data sources, such as text, images, and sensor data

Prof. Hiroshi Yamamoto
Chair of AI and Healthcare Informatics, University of Tokyo, Japan
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Published 30-12-2022

Keywords

  • Multi-modal Learning,
  • Heterogeneous Data Fusion,
  • Text-Image-Sensor Integration

How to Cite

[1]
Prof. Hiroshi Yamamoto, “Multi-modal Learning for Fusion of Heterogeneous Data: Investigating multi-modal learning approaches for integrating heterogeneous data sources, such as text, images, and sensor data”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 142–150, Dec. 2022, Accessed: Nov. 09, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/79

Abstract

Multi-modal learning has emerged as a powerful paradigm for handling the complexity and richness of modern data sources. This paper explores the fusion of heterogeneous data types, including text, images, and sensor data, through multi-modal learning approaches. We investigate the challenges and opportunities in integrating these data modalities and review state-of-the-art techniques for multi-modal fusion. We also discuss applications of multi-modal learning in various domains, highlighting its potential for enhancing data analysis and decision-making processes.

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