Session Abstract

Multimodal content understanding and analysis are essential for advancing intelligent transportation systems by integrating diverse data sources such as images, videos, text, audio, and sensor inputs. These capabilities enhance safety, efficiency, and decision-making processes. However, the complexity of modern transportation ecosystems introduces significant challenges, including the alignment of disparate data streams, accurate content interpretation, and privacy concerns. Key applications such as analyzing driver behavior through visual and audio cues, detecting traffic anomalies using sensor and video data, and correlating textual traffic reports with real-time images demand innovative multimodal learning approaches. This special session seeks to provide a platform for inspiring new research directions and exploring practical applications of multimodal analysis in intelligent transportation. By bridging diverse disciplines, this session aims to shape the future of multimodal research and accelerate real-world implementation in transportation systems.

Call for Papers

We invite original research papers for the ICME 2025 Special Session on AI4IT: Multimodal Content Analysis, Understanding, and Generation for Intelligent Transportation. This session focuses on addressing the challenges and opportunities in leveraging diverse data sources—such as images, videos, text, audio, and sensor inputs—for enhancing intelligent transportation systems.

Topics of Interest

• Multimodal learning frameworks for intelligent transportation systems
• Multimodal fusion techniques for enhanced traffic understanding
• Generative models for multimodal content generation and alignment
• Multimodal content retrieval and recommendation in transportation contexts
• Privacy and security concerns in multimodal data usage
• Cross-modal emotion and behavior analysis for driver and passenger safety
• Real-world multimodal datasets for intelligent transportation
• Explainable and interpretable multimodal models for transportation applications
• Ethical and policy-related considerations for multimodal data in transportation

programme

TBD

• Papers must be no longer than 6 pages, including all text, figures, and references.
• The abstract should be about 100-150 words.
• You can obtain more detailed information through this link.