SS12: Multimodal Content Understanding and Analysis in Intelligent Transportation
Motivation & significance
•Multimodal content understanding and analysis are crucial for enhancing intelligent transportation systems by leveraging diverse data sources such as images, videos, text, audio, and sensor inputs. These capabilities improve safety, efficiency, and decision-making processes.
•Provide a platform to inspire new research directions and explore practical applications of multimodal analysis in intelligent transportation.
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
Organizers
Xian Zhong, Professor, Wuhan University of Technology, China, email: hongx@whut.edu.cn
Wenxin Huang, Associate Professor, Hubei University, China, email: wenxinhuang_wh@163.com
Yifang Yin, Senior Scientist, Institute for Infocomm Research (I2R), Singapore, email: yin_yifang@i2r.a-star.edu.sg
Zheng Wang, Professor, Wuhan University, China, email: wangzwhu@whu.edu.cn
Chia-Wen Lin, Professor, National Tsing Hua University, Taiwan, email: cwlin@ee.nthu.edu.tw