Summary of Knowledge Graphs Meet Multi-modal Learning: a Comprehensive Survey, by Zhuo Chen et al.
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
by Zhuo Chen, Yichi Zhang, Yin Fang, Yuxia Geng, Lingbing Guo, Xiang Chen, Qian Li, Wen Zhang, Jiaoyan Chen, Yushan Zhu, Jiaqi Li, Xiaoze Liu, Jeff Z. Pan, Ningyu Zhang, Huajun Chen
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive survey on Knowledge Graphs (KGs) and their applications in multi-modal learning. The authors review over 300 articles, focusing on two key aspects: KG-driven Multi-Modal (KG4MM) learning and Multi-Modal Knowledge Graph (MM4KG). They explore the construction of KGs and MMKGs, highlighting research trajectories for tasks such as image classification, visual question answering, and multi-modal knowledge graph completion. The survey also provides definitions, evaluation benchmarks, and essential insights for conducting relevant research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about how knowledge graphs can help with different AI applications that involve multiple types of data, like images and text. The authors look at many research papers on this topic, focusing on two main areas: using knowledge graphs to improve multi-modal learning, and extending knowledge graph studies to include multiple modalities. They also explore how knowledge graphs are built and highlight key research directions. |
Keywords
* Artificial intelligence * Image classification * Knowledge graph * Multi modal * Question answering
