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Machine learningDeep learning / NLP / CV

Multimodal Word2Vec

Multimodal Word2Vec huongeza mfumo wa kawaida wa Word2Vec kwa kutia nanga uwakilishi wa maneno katika ishara za hisi — kwa kawaida vipengele vya picha — pamoja na takwimu za maandishi zinazosambazwa. Matokeo yake ni vekta za maneno zinazokamata ruwaza za kutokea pamoja kwa lugha na maana ya kuona, kuwezesha hukumu tajiri za kufanana kwa maana na utendaji bora zaidi kwenye kazi za ngazi ya dhana ambapo upungufu wa upungufu wa maandishi pekee.

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Vyanzo

  1. Bruni, E., Tran, N.-K., & Baroni, M. (2014). Multimodal Distributional Semantics. Journal of Artificial Intelligence Research, 49, 1–47. DOI: 10.1613/jair.4135
  2. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems (NIPS), 26. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Multimodal Word2Vec (Cross-Modal Distributional Semantics). ScholarGate. https://scholargate.app/sw/deep-learning/multimodal-word2vec

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Imerejelewa na

ScholarGateMultimodal Word2Vec (Multimodal Word2Vec (Cross-Modal Distributional Semantics)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/multimodal-word2vec · Seti ya data: https://doi.org/10.5281/zenodo.20539026