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	Wielomodalna grafowa sieć neuronowa	Klasyfikacja multimodalna oparta na BERT
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2019–2020	2019
Twórca	Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020	Kiela, D. et al.; Lu, J. et al.
Typ	Graph-based deep learning with multimodal input fusion	Multimodal transformer classifier
Źródło pierwotne	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.
Inne nazwy	MM-GNN, Multimodal GNN, Multi-modal Graph Network, Cross-modal Graph Neural Network	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Pokrewne	6	2
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 BERT-based classification extends the BERT transformer architecture to jointly encode and classify data from multiple modalities — most commonly text paired with images — by fusing their representations before a final classification head. Introduced prominently around 2019 through models such as MMBT and ViLBERT, it has become a standard approach for tasks where neither text nor image alone carries sufficient information for accurate labeling.