ICDAR'22: Intelligent Cross-Data Analysis and Retrieval

Abstract

We have witnessed the rise of cross-data against multimodal data problems recently. The cross-modal retrieval system uses a textual query to look for images; the air quality index can be predicted using lifelogging images; the congestion can be predicted using weather and tweets data; daily exercises and meals can help to predict the sleeping quality are some examples of this research direction. Although vast investigations focusing on multimodal data analytics have been developed, few cross-data (e.g., cross-modal data, cross-domain, cross-platform) research has been carried on. In order to promote intelligent cross-data analytics and retrieval research and to bring a smart, sustainable society to human beings, the specific article collection on "Intelligent Cross-Data Analysis and Retrieval" is introduced. This Research Topic welcomes those who come from diverse research domains and disciplines such as well-being, disaster prevention and mitigation, mobility, climate change, tourism, healthcare, and food computing.

Yuta Nakashima

Professor

Yuta Nakashima is a professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.