Multimodal Data Fusion and Computational Optimization for Intelligent Perception

Dear Colleagues, 

The concept of multimodal data fusion was introduced to break down the research barriers of intelligent perception using single modality and brings great application prospects. Studies on multimodality have attracted much attention from both academia and the industry. Multisource sensors play a complementary role, being able to supply information for single sensor systems in complex environments. The ability of perception, interaction, and intelligent collaboration with multimodal fusion technology has improved the intelligence level of various applications, helping to realize higher precision in dynamic scenes based on the computing power of accelerator platforms. Multimodal data fusion is now applied to all areas of intelligent perception, providing a new path for further improvements in perception and understanding. Research on multimodal data fusion has driven the application of related algorithms and techniques in areas such as automatic driving, remote sensing, and industrial internet. Although multimodal data fusion brings multiple benefits to a variety of applications, it still faces many problems and challenges, such as insufficient fused data types, highly customized fusion methods, and time-consuming fusion algorithms. The methodology of multimodal fusion and its applications also need to be further optimized. We look forward to the latest research findings that suggest theories and practical solutions for multimodal data fusion and its applications.

Suggested Topics:

The topics of interest include but are not limited to:

• Methodology of multimodal fusion;
• Visible and infrared image fusion;
• Image and point cloud fusion;
• Radar–camera fusion;
• Autopilot multimode sensing;
• Multimodal remote sensing data fusion and application;
• Object detection and semantic segmentation;
• Anomaly detection;
• Design of a lightweight multi-mode fusion method;
• Application of multi-modal data fusion.

Prof. Dr. Tao Shen
Prof. Dr. Lei Zhang
Prof. Dr. John Wang
Guest Editors

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