Сравнение методов

	Мультимодальная графовая нейронная сеть	Мультимодальный трансформер
Область	Глубокое обучение	Глубокое обучение
Семейство	Machine learning	Machine learning
Год появления	2019–2020	2019–2021
Автор метода	Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020	Lu et al. (ViLBERT); Radford et al. (CLIP)
Тип	Graph-based deep learning with multimodal input fusion	Cross-modal attention-based deep learning model
Основополагающий источник	Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR).	Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32.
Другие названия	MM-GNN, Multimodal GNN, Multi-modal Graph Network, Cross-modal Graph Neural Network	multimodal attention model, cross-modal transformer, vision-language transformer, multi-modal fusion transformer
Связанные	6	5
Сводка	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.	A Multimodal Transformer extends the standard Transformer architecture to process and jointly reason over two or more input modalities — most commonly text and images, but also audio, video, or structured data. Cross-modal attention layers allow information from one modality to inform representations in another, enabling tasks such as visual question answering, image captioning, and multimodal sentiment analysis.