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| Multimodálne Word2Vec× | Multimodálny Doc2Vec× | |
|---|---|---|
| Odbor | Hlboké učenie | Hlboké učenie |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2014 | 2014–2017 |
| Tvorca≠ | Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.) | Le, Q. V. & Mikolov, T. (Doc2Vec core); multimodal extensions by various authors post-2014 |
| Typ≠ | Multimodal word embedding model | Multimodal document embedding |
| Pôvodný zdroj≠ | Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47. DOI ↗ | Le, Q. V., & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), PMLR 32(2), 1188–1196. link ↗ |
| Ďalšie názvy | multimodal word embeddings, visual-linguistic Word2Vec, cross-modal Word2Vec, MM-W2V | Multimodal Paragraph Vector, Cross-modal Doc2Vec, Multi-source PV-DM, Multimodal Document Embedding |
| Príbuzné≠ | 5 | 6 |
| Zhrnutie≠ | 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 Doc2Vec extends the Doc2Vec paragraph-vector framework to incorporate information from more than one modality — typically text alongside images, audio, or structured metadata — producing a shared document-level embedding that captures semantics from multiple sources simultaneously. It is used for cross-modal retrieval, multi-source classification, and document representation where text alone is insufficient. |
| ScholarGateDátová sada ↗ |
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