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	Wielomodalna grafowa sieć neuronowa	Osadzenia zdań multimodalnych
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2019–2020	2013–2021
Twórca	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)
Typ	Graph-based deep learning with multimodal input fusion	Representation learning model
Źródło pierwotne	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	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.
Inne nazwy	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
Pokrewne	6	1
Podsumowanie	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.