◕ Track 1: Machine Learning Methods and Computational Intelligence

• Deep Neural Networks and Model Architectures
  

• Reinforcement Learning and Sequential Decision-Making

• Self-supervised and Representation Learning

• Transfer Learning and Domain Adaptation

• Multi-task and Lifelong Learning

• Probabilistic Learning and Bayesian Inference

• Optimization Algorithms for Machine Learning

• Trustworthy, Robust and Explainable AI

◕ Track 2: Perception, Sensing and Multimodal Intelligence

• Computer Vision and Scene Understanding
  

• Speech Processing and Natural Language Understanding

• Multimodal Learning and Cross-modal Fusion

• Sensor Data Processing and Information Fusion

• Active Perception and Sensorimotor Coordination

• Human Behavior Analysis and Intent Recognition

• Spatiotemporal Data Modeling

• Perception for Autonomous and Intelligent Systems

  
  

◕Track 3: Embodied Intelligence, Robotics and Autonomous Systems

• Robot Learning and Intelligent Control

• Embodied Perception-Action Learning

• Vision-Language-Action Models

• World Models and Environment Representation

• Autonomous Navigation and Motion Planning

• Dexterous Manipulation and Tool Use

• Human-Robot Interaction and Collaboration

• Sim-to-Real Transfer and Adaptive Robotics

◕ Track 4: Intelligent Systems and Cyber-Physical Applications

• Intelligent Control and Automation Systems
  

• Cyber-Physical Systems and Digital Twin

• Edge Intelligence and Distributed Systems

• Internet of Things for Intelligent Systems

• Smart Manufacturing Systems

• Intelligent Transportation Systems

• AI-enabled Healthcare Systems

• Safety and Reliability of Cyber-Physical Systems