In recent years, there has been significant growth in online multimodal data, such as images, videos, text, or audio clips. To understand these heterogeneous data, intelligent cross-media analysis of multimodal data has become a topic of increasing in the fields of artificial intelligence (AI) and multimedia computing. The goal of cross-media data analysis is to understand physical objects and symbolic concepts, infer the relationships between entities extracted from the multimedia, and discover knowledge hidden in multimodal data. Based on cross-media data analytics, AI applications are expected to “think” like the human brain and make explainable and trustable decisions.

Cross-media data analytics aims to deeply understand and precisely compute the intrinsic attributes of entities extracted from multimodal data, as well as their association with other interactive entities. Recently, deep neural networks (DNNs) have become the standard solution to various intelligent computing problems. DNNs aim to emulate the human learning approach to acquire specific types of knowledge and provide a suitable AI tool for intelligent computing of high-order relations between cross-modal data. Academics are paying increasing attention to DNNs, which may significantly enhance the effectiveness of cross-media data analytics for intelligent computing tasks.

This Special Issue therefore intends to report innovative and high-quality solutions that will advance research into cross-media data analytics for multimedia data. We welcome high-quality, influential, and original papers on recent advances in the emerging research topics of cross-media data analytics in the field of intelligent computing, as well as their applications in specific domains. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Cross-media data analytics based on DNNs
• Explainable cross-media data analytics
• Knowledge-driven cross-media data analytics
• Multimodal data analytics based on DNNs
• Multimodal data intelligent computing
• Multimodal knowledge graph construction for cross-media data analytics
• Domain-specific applications of cross-media data analytics