CFP last date
21 July 2025
Reseach Article

Multimodal Deep Learning: A Survey of Models, Fusion Strategies, Applications, and Research Challenges

by Sai Teja Erukude, Suhasnadh Reddy Veluru, Viswa Chaitanya Marella
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 19
Year of Publication: 2025
Authors: Sai Teja Erukude, Suhasnadh Reddy Veluru, Viswa Chaitanya Marella
10.5120/ijca2025925264

Sai Teja Erukude, Suhasnadh Reddy Veluru, Viswa Chaitanya Marella . Multimodal Deep Learning: A Survey of Models, Fusion Strategies, Applications, and Research Challenges. International Journal of Computer Applications. 187, 19 ( Jul 2025), 1-7. DOI=10.5120/ijca2025925264

@article{ 10.5120/ijca2025925264,
author = { Sai Teja Erukude, Suhasnadh Reddy Veluru, Viswa Chaitanya Marella },
title = { Multimodal Deep Learning: A Survey of Models, Fusion Strategies, Applications, and Research Challenges },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2025 },
volume = { 187 },
number = { 19 },
month = { Jul },
year = { 2025 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number19/multimodal-deep-learning-a-survey-of-models-fusion-strategies-applications-and-research-challenges/ },
doi = { 10.5120/ijca2025925264 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-07-09T01:07:44.264081+05:30
%A Sai Teja Erukude
%A Suhasnadh Reddy Veluru
%A Viswa Chaitanya Marella
%T Multimodal Deep Learning: A Survey of Models, Fusion Strategies, Applications, and Research Challenges
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 19
%P 1-7
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Multimodal deep learning has become a primary methodological framework in artificial intelligence, allowing models to learn from (and reason over) many different types of data, such as text, images, audio, and video. By utilizing multiple modalities simultaneously, systems can enhance their contextual understanding, noise resilience, and generalization, all of which closely resemble human perception. This review offers a comprehensive overview of the field, taking a look at the basics of modality integration, fusion methods (early, late, and hybrid), and some of the main architectural advances in models like CLIP, Flamingo, GPT-4V, Gemini 1.5, and AudioCLIP. It also provides a primer on real-world applications in healthcare, autonomous systems, robotics, and education, including benchmarking datasets and evaluation metrics essential for evaluating performance. Notable challenges, such as modality imbalance, scalability, and interoperability, are highlighted, while also looking at growing areas of interest such as long-context modeling and embodied intelligence. As a review survey, the goal is to provide a map of options for researchers and practitioners who want to enhance their use of multimodal AI systems, both in research and in actual deployment.

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Index Terms

Computer Science
Information Sciences

Keywords

Cross-Modal Learning Fusion Strategies Vision-Language Reasoning Multimodal Architectures Foundation Models