◕ Artificial Intelligence and Machine Learning for Multimodal Systems

• Machine learning and deep learning

• Computer vision and natural language processing

• Generative AI and large-scale models

• Multimodal machine learning and vision-language modeling

• Representation learning for multimodal data

• Reinforcement learning and intelligent decision-making

• Explainable, trustworthy and robust

◕ Multimodal Signal Processing and Representation

• Image, video, speech and audio signal processing

• Sensor data processing and multimodal sensing

• Signal enhancement, restoration and compression

• Multimodal data encoding and representation

• Biomedical and physiological signal analysis

• Multimodal time-series analysis

◕ Cross-Modal Learning and Data Fusion

• Cross-modal data fusion

• Cross-modal alignment and representation mapping

• Multimodal deep learning architectures

• Multimodal reasoning and decision-making

• Attention mechanisms in multimodal learning

• Self-supervised and unsupervised multimodal learning

• Knowledge-guided multimodal learning

◕ Multimodal Intelligent Systems and Applications

• Intelligent perception systems

• Human–machine interaction and immersive systems

• Intelligent robotics and autonomous systems

• Multimodal AI for autonomous driving and transportation

• Industrial AI and smart manufacturing

• Multimodal AI for IoT and edge computing

• AI for healthcare and biomedical applications

• Real-time multimodal system integration and deployment