Comparer des méthodes

	Réseau de neurones convolutifs multimodaux	Classification multimodale basée sur BERT
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2011	2019
Auteur d'origine	Ngiam, J. et al. / multiple groups	Kiela, D. et al.; Lu, J. et al.
Type	Multimodal deep learning model	Multimodal transformer classifier
Source fondatrice	Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., & Ng, A. Y. (2011). Multimodal deep learning. In Proceedings of the 28th International Conference on Machine Learning (ICML), 689–696.	Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950.
Alias	MM-CNN, multimodal CNN, multi-input CNN, cross-modal convolutional network	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Apparentées	5	2
Résumé	A Multimodal Convolutional Neural Network (MM-CNN) processes and fuses two or more input modalities — such as images and text, or video and audio — through dedicated convolutional branches, learning a shared representation that captures complementary signals from each source. The fused representation drives a downstream task such as classification, regression, or retrieval.	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.