Linganisha mbinu

	Mtandao wa Neurali wa Mwingiliano wa Njia Nyingi	Transformeri wa Multimodal
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2019–2020	2019–2021
Mwanzilishi	Kipf & Welling (GNN foundation); extended to multimodal settings by multiple research groups c. 2019–2020	Lu et al. (ViLBERT); Radford et al. (CLIP)
Aina	Graph-based deep learning with multimodal input fusion	Cross-modal attention-based deep learning model
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	6	5
Muhtasari	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.