Call for Papers for a Special Issue on "Multi-modal Information Learning and Analytics on Cross-Media Data Integration"
Aims and Scope of the Special Issue
We are living in the era of data deluge. Meanwhile, the world of big data exhibits a rich and complex set of cross-media contents, such as text, image, video, audio and graphics. Thus far, great research efforts have been separately dedicated to big data processing and cross-media mining, with well theoretical underpinnings and great practical success. However, studies jointly considering cross-media big data analytics are relatively sparse. This research gap needs our more attention, since it will benefit lots of real-world applications. Despite its significance and value, it is non-trivial to analyze cross-media big data due to their heterogeneity, large-scale volume, increasing size, unstructured, correlations, and noise. Multi-modal Information Learning, which can be treated as the most significant breakthrough in the past 10 years, has greatly affected the methodology of computer vision and achieved terrific progress in both academy and industry.
This special issue focuses on learning methods to achieve high performance Multi-modal Information analysis and understanding under uncontrolled environments in large scale, which is also a very challenging problem. Moreover, it attracts much attention from both the academia and the industry. We hope this topic will aggregate top level works on the new advances in Multi-modal Information from cross-media data.
Topics of interests include, but are not limited to:
- Cross-Media Big Data Representation
- Large-scale multimodal media data acquisition
- Novel dataset and benchmark for cross-media big data analytics
- Cross-Media Big Data Management
- Large-scale multimodal information fusion
- Domain adaptation for cross-media big data
- Cross-media big data organization, retrieval and indexing
- Learning methods to bridge the semantic gap among media types
- Cross-Media Big Data Understanding and Applications
- Multi-modal Information for feature representation
Submissions for this special issue should be submitted through the Journal’s submission system, Editorial Manager. Detailed guidelines on submission format and process can be found in the Instructions for Authors of the journal.
Submission deadline: October 1, 2025
Guest Editors:
Xu Zheng Shanghai Polytechnic University, China Xu Zheng is currently the professor in Shanghai Polytechnic University, China. He is also the associate editor of Springer ECR journal and Springer DIoT journal.
Jemal H. Abawajy Deakin University, Australia Jemal H. Abawajy is a Full Professor with the School of Information Technology, Faculty of Science, Engineering, and Built Environment, Deakin University, Australia. He is currently the Director of the Parallel and Distributing Computing Laboratory.
Shafi’i Muhammad Abdulhamid Federal University of Technology, Minna-Nigeria Shafi’i Muhammad Abdulhamid received his Ph.D. in Computer Science from University of Technology Malaysia (UTM). Presently, he is the professor of FEDERAL UNIVERSITY OF TECHNOLOGY, MINNA-NIGERIA.
Haruna Chiroma University of Hafr Al Batin, Saudi Arabia Haruna Chiroma received the Ph.D. degree from the Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya. He is currently Professor (Assistant) at University of Hafr Al Batin.