Techniques and Applications of Multimodal Data Fusion

Dear Colleagues,

Multimodal data fusion is a transformative area of artificial intelligence research, dedicated to integrating diverse data modalities—such as visual, linguistic, auditory, and sensory inputs—into unified frameworks. This integration enables systems to achieve more comprehensive understanding and robust decision making across complex, dynamic scenarios. Advances in machine learning, particularly in deep neural networks, have paved the way for innovative algorithms and methodologies, pushing the boundaries of multimodal representation learning, cross-modal reasoning, and data alignment techniques.

Despite these advancements, significant challenges persist. Issues such as data heterogeneity, noisy inputs, semantic misalignment, and the scalability of fusion methods limit the applicability of current approaches in real-world environments. Furthermore, evaluating the performance and robustness of multimodal systems remains a critical research area, requiring novel benchmarks and metrics.

This Special Issue aims to provide a platform for exploring state-of-the-art techniques, novel theoretical contributions, and diverse applications in multimodal data fusion. Suggested topics include, but are not limited to, the following:

• Advanced algorithms for feature extraction, multimodal representation learning, and cross-modal alignment.
• Scalable and robust fusion techniques capable of handling noisy, sparse, and high-dimensional data.
• Integration of multimodal fusion in diverse applications, such as autonomous systems, personalized healthcare, education, and urban infrastructure.
• New frameworks for evaluating multimodal systems, emphasizing robustness, interpretability, and computational efficiency.
• Exploration of emergent fields, such as social science analytics, human-centric AI, and creative AI, through the lens of multimodal fusion.

While these topics highlight promising directions, we also encourage submissions that go beyond the current scope and propose innovative ideas from broader dimensions. Contributions bridging disciplines or presenting disruptive perspectives are particularly welcomed.

The interdisciplinary nature of multimodal data fusion fosters collaboration between computer science, engineering, social sciences, and even the arts. This cross-disciplinary approach holds the potential to redefine how intelligent systems interact with and understand the world. From enabling advanced vision–language interfaces to facilitating sustainable smart cities, the applications are vast and impactful.

We invite researchers from diverse backgrounds to contribute original research and reviews that push the boundaries of this exciting field. Through this Special Issue, we aim to create a platform for collaboration, innovation, and exploration, driving forward the development of multimodal data fusion to address both theoretical and practical challenges in the years to come.

Dr. Shiyu Hu
Dr. Wenjie Yang
Dr. Zhaorui Zhang
Dr. Shengli Wu
Guest Editors

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• multimodal data fusion
• multimodal representation learning
• robust fusion techniques
• feature alignment
• cross-modal reasoning
• heterogeneous data integration