Foundations and Theories of Multimodal AI
Multimodal representation learning and cross-modal alignment
Unified multimodal modeling theories and architectures
Pretraining and post-training methods for multimodal large models
World models and physical law learning
Self-supervised, semi-supervised, and few-shot multimodal learning
Explainable and trustworthy multimodal learning
Robust learning and uncertainty modeling
Cross-modal generation and reasoning
Autonomous Agents, Multi-Agent Collaboration, and Human-AI Hybrid Decision-Making
Autonomous agent theory and self-evolution mechanisms
Multi-agent collaboration and swarm intelligence
Autonomous planning and scheduling in complex dynamic environments
Reinforcement learning and autonomous decision-making
Multimodal intention understanding and human-robot interaction
Human-AI hybrid augmented intelligence and human-in-the-loop mechanisms
Embodied AI and physical interaction
Safety, ethics, and alignment in human-AI collaboration
Multimodal AI System and Implementation Technologies
Architectural design and optimization of multimodal intelligent systems
Edge-cloud collaboration and engineering deployment
Model lightweighting, compression, and embedded optimization
Multimodal data governance, evaluation, and synthetic data
Digital twin and simulation verification technologies
Large-scale data processing and distributed learning
System reliability, robustness, and fault diagnosis
Privacy preservation and security protection technologies
Cutting-Edge Applications and Interdisciplinary Intersections of Multimodal AI
Multimodal AI for scientific discovery (AI4Science)
Multimodal learning in healthcare and bioinformatics
Cross-disciplinary multimodal data analysis
Industrial manufacturing and digital twin applications
Multimodal AI in low-resource and real-world scenarios
Cross-scenario transfer learning and domain adaptation
Societal impacts and responsible AI in multimodal systems
Intelligent decision support and industry applications