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| Daudzmodālu Doc2Vec× | Multimodāls Word2Vec× | |
|---|---|---|
| Nozare | Dziļā mācīšanās | Dziļā mācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2014–2017 | 2014 |
| Autors≠ | Le, Q. V. & Mikolov, T. (Doc2Vec core); multimodal extensions by various authors post-2014 | Bruni, E., Tran, N.-K., & Baroni, M. (building on Mikolov et al.) |
| Tips≠ | Multimodal document embedding | Multimodal word embedding model |
| Pirmavots≠ | 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 ↗ | Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47. DOI ↗ |
| Citi nosaukumi | Multimodal Paragraph Vector, Cross-modal Doc2Vec, Multi-source PV-DM, Multimodal Document Embedding | multimodal word embeddings, visual-linguistic Word2Vec, cross-modal Word2Vec, MM-W2V |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
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