Sammenlign metoder

	Multimodal Word2Vec	Multimodale setningsinnleiringer
Fagfelt	Dyp læring	Dyp læring
Familie	Machine learning	Machine learning
Opprinnelsesår	2014	2013–2021
Opphavsperson	Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.)	Frome et al. (DeViSE, 2013); popularized by Radford et al. (CLIP, 2021)
Type	Multimodal word embedding model	Representation learning model
Opprinnelig kilde	Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47.	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.
Alias	multimodal word embeddings, visual-linguistic Word2Vec, cross-modal Word2Vec, MM-W2V	multimodal embeddings, cross-modal sentence embeddings, vision-language embeddings, joint image-text embeddings
Relaterte	5	1
Sammendrag	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 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.