方法对比
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| 多模态图神经网络× | 多模态句子嵌入× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2019–2020 | 2013–2021 |
| 提出者≠ | Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020 | Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021) |
| 类型≠ | Graph-based deep learning with multimodal input fusion | Representation learning model |
| 开创性文献≠ | Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR). link ↗ | Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning (ICML), pp. 8748–8763. PMLR. link ↗ |
| 别名 | MM-GNN, Multimodal GNN, Multi-modal Graph Network, Cross-modal Graph Neural Network | multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings |
| 相关≠ | 6 | 1 |
| 摘要≠ | A Multimodal Graph Neural Network (MM-GNN) combines data from multiple modalities — such as text, images, and structured features — into a unified graph structure and applies graph-based message passing to learn joint representations. It enables relational reasoning across heterogeneous data sources, going beyond what unimodal or simple concatenation approaches can capture. | Multimodal sentence embeddings map text and images (and sometimes audio or video) into a shared continuous vector space, so that semantically related pairs from different modalities land close together. Trained by contrastive objectives on large paired corpora, these representations power cross-modal retrieval, zero-shot classification, and vision-language reasoning. |
| ScholarGate数据集 ↗ |
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