Multimodal Vision: Mathematical Foundations, Models, and Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 14 June 2026 | Viewed by 13
Special Issue Editor
Special Issue Information
Dear Colleagues,
The Special Issue "Multimodal Vision: Mathematical Foundations, Models, and Applications" explores the integration of diverse data modalities (e.g., images, text, audio, and sensor data) to advance computer vision systems. By combining multiple sources of information, multimodal approaches enhance robustness, accuracy, and contextual understanding, addressing complex real-world challenges. This Issue focuses on foundational theories, novel algorithms, and cutting-edge applications that bridge gaps between heterogeneous data types. Topics include multimodal fusion techniques, cross-modal learning, self-supervised methods, and interpretability in multimodal models. Applications span autonomous systems, healthcare (e.g., medical imaging), robotics, augmented reality, and environmental monitoring. Contributions are encouraged to address challenges such as scalability, data heterogeneity, and real-time processing, while leveraging advances in deep learning, graph neural networks, and transformer architectures. The Issue aims to foster interdisciplinary research, uniting mathematics, computer science, and engineering to push the boundaries of multimodal vision systems.
I look forward to receiving your contributions.
Dr. Xueping Wang
Guest Editor
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Keywords
- multimodal learning
- computer vision
- deep learning
- data fusion
- cross-modal retrieval
- sensor fusion
- autonomous systems
- medical imaging
- neural networks
- multimodal representation learning
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