Uporedite metode

	Multimodal Word2Vec	Multimodalna klasifikacija zasnovana na BERT-u
Oblast	Duboko učenje	Duboko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	2014	2019
Tvorac	Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.)	Kiela, D. et al.; Lu, J. et al.
Tip	Multimodal word embedding model	Multimodal transformer classifier
Temeljni izvor	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.
Drugi nazivi	multimodal word embeddings, visual-linguistic Word2Vec, cross-modal Word2Vec, MM-W2V	MMBT, multimodal transformer classification, BERT multimodal fusion, vision-language BERT classifier
Srodne	5	2
Sažetak	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.