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
Published 30-12-2022
Keywords
- Multi-modal Learning,
- Heterogeneous Data Fusion,
- Text-Image-Sensor Integration
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
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.
Downloads
References
- Tatineni, Sumanth. "Customer Authentication in Mobile Banking-MLOps Practices and AI-Driven Biometric Authentication Systems." Journal of Economics & Management Research. SRC/JESMR-266. DOI: doi. org/10.47363/JESMR/2022 (3) 201 (2022): 2-5.
- Vemori, Vamsi. "Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis." Advances in Deep Learning Techniques 1.1 (2021): 23-57.
- Mahammad Shaik, et al. “Unveiling the Achilles’ Heel of Decentralized Identity: A Comprehensive Exploration of Scalability and Performance Bottlenecks in Blockchain-Based Identity Management Systems”. Distributed Learning and Broad Applications in Scientific Research, vol. 5, June 2019, pp. 1-22, https://dlabi.org/index.php/journal/article/view/3.
- Tatineni, Sumanth. "INTEGRATING AI, BLOCKCHAIN AND CLOUD TECHNOLOGIES FOR DATA MANAGEMENT IN HEALTHCARE." Journal of Computer Engineering and Technology (JCET) 5.01 (2022).