Zhuo Chen , Yichi Zhang , Yin Fang , Yuxia Geng , Lingbing Guo , Jiaoyan Chen , Xiaoze Liu , Jeff Z. Pan , Ningyu Zhang , Huajun Chen , Wen Zhang
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引用次数: 0
Abstract
Integrated with multi-modal learning, knowledge graphs (KGs) as structured knowledge repositories, can enhance AI for processing and understanding complex, real-world data. This paper provides a comprehensive survey of cutting-edge research on KG-aware multi-modal learning. For these core areas, we provide task definitions, evaluation benchmarks, and comprehensive insights into key breakthroughs, offering detailed explanations critical for conducting related research. Furthermore, we also discuss current challenges, highlighting emerging trends and future research directions. The repository for this paper can be found at https://github.com/zjukg/KG-MM-Survey.
期刊介绍：
Information Fusion serves as a central platform for showcasing advancements in multi-sensor, multi-source, multi-process information fusion, fostering collaboration among diverse disciplines driving its progress. It is the leading outlet for sharing research and development in this field, focusing on architectures, algorithms, and applications. Papers dealing with fundamental theoretical analyses as well as those demonstrating their application to real-world problems will be welcome.
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