Multimodal Pattern Recognition and Learning for Intelligent Systems

Dear Colleagues,

Multimodal pattern recognition has become a cornerstone of modern artificial intelligence, driven by the increasing availability of diverse data modalities such as images, videos, text, speech, and sensory signals. By integrating complementary information from multiple sources, it is possible to achieve more robust and accurate recognition systems. Meanwhile, advances in multimodal learning have enabled intelligent systems to perceive, reason, and interact with complex real-world environments across applications ranging from autonomous driving to healthcare and embodied AI.

However, significant challenges remain in multimodal pattern recognition. These include modality alignment, fusion strategies, data sparsity, scalability issues, as well as the deployment of computationally intensive models on resource-constrained edge devices. The interpretability of multimodal systems and low-resource learning scenarios also demand further investigation. Contributions addressing these challenges are very welcome.

This Special Issue aims to bring together cutting-edge research on pattern recognition and multimodal learning for intelligent systems. Both theoretical advancements and real-world applications fall within the scope of this Special Issue.

Dr. Lei Wang
Prof. Jing Bai
Guest Editors

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