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	Word2Vec Multimodale	Classificazione basata su BERT multimodale
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2014	2019
Ideatore	Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.)	Kiela, D. et al.; Lu, J. et al.
Tipo	Multimodal word embedding model	Multimodal transformer classifier
Fonte seminale	Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47.	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	multimodal word embeddings, visual-linguistic Word2Vec, cross-modal Word2Vec, MM-W2V	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Correlati	5	2
Sintesi	Multimodal Word2Vec extends the classic Word2Vec framework by grounding word representations in perceptual signals — typically image features — alongside distributional text statistics. The result is word vectors that capture both linguistic co-occurrence patterns and visual meaning, enabling richer semantic similarity judgements and better performance on concept-level tasks where purely text-based embeddings fall short.	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.