Multimodal Fusion and Its Application

Dear Colleagues,

In the era of big data, information from a single source is often incomplete or biased. Multimodal fusion has thus emerged as a pivotal research frontier, aiming to develop computational techniques that integrate heterogeneous data from diverse sources—such as visual, auditory, textual, physiological, and genomic signals—to create more robust, accurate, and intelligent systems. By synergistically combining complementary information, multimodal fusion transcends the limitations of unimodal analysis, enabling a more holistic and nuanced understanding of complex phenomena.

This Special Issue, "Multimodal Fusion and Its Application," seeks to compile cutting-edge research that addresses both the theoretical foundations and practical implementations of fusion strategies. We welcome contributions that explore novel architectures based on deep learning, graph neural networks, and transformer models, which can effectively align and fuse data from different temporal, spatial, and semantic scales. A key focus is on enhancing the interpretability, robustness, and data-efficient learning of these fusion models.

Beyond methodological innovations, this Special Issue highlights the transformative applications of multimodal fusion across a spectrum of fields. We invite papers demonstrating significant advancements in areas including, but not limited to, biomedical diagnostics and personalized medicine, autonomous driving and intelligent robotics, human–computer interaction, affective computing, and precision agriculture. By bridging the gap between algorithmic development and real-world impact, this collection aims to serve as a comprehensive resource for researchers and practitioners, fostering the next generation of integrated intelligent systems. We invite original research and review articles that contribute to this dynamic and rapidly evolving domain.

Dr. Zhongtian Sun
Dr. Shuyan Li
Guest Editors

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