Krahasoni metodat

	Rrjeti Neurale Multimodal i Grafëve	Embëdime të frazave multimodalë
Fusha	Mësimi i thellë	Mësimi i thellë
Familja	Machine learning	Machine learning
Viti i origjinës	2019–2020	2013–2021
Krijuesi	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)
Lloji	Graph-based deep learning with multimodal input fusion	Representation learning model
Burimi themelues	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.
Emërtime të tjera	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
Të lidhura	6	1
Përmbledhja	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.