Deep Learning for Cross-Media Analysis and Knowledge Discovery

Nowadays, there are lots of heterogeneous and homogeneous media data from different sources, such as news media websites, mobile phones, social networking websites, TikTok, etc. Integrated together, these media data represent different aspects of the real-world and help document the evolution of the world. Consequently, it is impossible to correctly conceive and to appropriately understand the world without exploiting the data available on these different sources of rich multimedia content simultaneously and synergistically. Cross-media analysis is a research area in the general field of multimedia content analysis that focuses on the exploitation of data with different modalities from multiple sources simultaneously and synergistically to discover knowledge and understand the world. 

As this research area is becoming increasingly integrated into our daily lives, there is a significant amount of research interest among different communities in improving cross-media analysis and knowledge discovery, and various novel solutions have been proposed to address different real-world applications. In this special issue of Intelligent Computing, a Science Partner Journal (SPJ), we hope to bring together community research in this area into a curated selection of articles. This special issue allows both Research Articles and Review Articles about cross-media analysis and knowledge discovery. More detailed submission instructions, e.g., manuscript format and submission guidance, are provided on the “For Authors” page. 

Note that the exceptional merits and advantages of this special issue are: very quick publication (less than 2 months after submission, if acceptable), Open Access to widely global readers via various prestigious AAAS & SPJ-enabled pipelines, and no APCs (article processing charges) until 2025.

This special issue is dedicated to the techniques for cross-media analysis and knowledge discovery. Topics of interest include, but are not limited to:

• Explainable cross-media data analytics
• Knowledge-driven cross-media data analytics
• Multimodal data analytics with deep learning
• Multimodal data intelligent computing
• Multimodal knowledge graph construction for cross-media data analytics
• Domain-specific applications of cross-media data analytics and knowledge discovery

Hehe Fan, National University of Singapore, Singapore
Xiaojun Chang, University of Technology Sydney, Australia
Yi Yang, Zhejiang University, China
Alex Hauptmann, Carnegie Mellon University, USA

The timeline for the submission and review process is as follows (all deadlines are 23:59 (11:59PM) anywhere on earth).

Invitation acceptance: 6 June 2022
Paper submission: 30 September 2022
Review notification: 16 November 2022
Reviewed submission: 5 December 2022

Please indicate in your cover letter that your submission is intended for inclusion in the special issue on Deep Learning for Cross-Media Analysis and Knowledge Discovery.