Summary of When Graph Meets Multimodal: Benchmarking and Meditating on Multimodal Attributed Graphs Learning, by Hao Yan et al.
When Graph meets Multimodal: Benchmarking and Meditating on Multimodal Attributed Graphs Learning
by Hao Yan, Chaozhuo Li, Jun Yin, Zhigang Yu, Weihao Han, Mingzheng Li, Zhengxin Zeng, Hao Sun, Senzhang Wang
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a comprehensive benchmark dataset, MAGB, for multimodal attributed graphs (MAGs), featuring curated graphs from various domains with both textual and visual attributes. The authors evaluate two mainstream MAG representation learning paradigms: GNN-as-Predictor, which integrates multimodal attributes via Graph Neural Networks (GNNs), and VLM-as-Predictor, which harnesses Vision Language Models (VLMs) for zero-shot reasoning. Their experiments on MAGB reveal that modality significances fluctuate drastically with specific domain characteristics, multimodal embeddings can elevate the performance ceiling of GNNs, but intrinsic biases among modalities may impede effective training, particularly in low-data scenarios. VLMs are highly effective at generating multimodal embeddings that alleviate the imbalance between textual and visual attributes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special dataset for understanding complex networks with many types of information (text, images, etc.). They test two different ways to learn from these networks: one using graph neural networks and another using vision language models. Their results show that what matters most is the type of data they’re working with. They also found that combining multiple types of data can make their models better, but there are still some challenges to overcome. |
Keywords
» Artificial intelligence » Gnn » Representation learning » Zero shot
