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Machine learningDeep learning / NLP / CV

Penyematan Zarah Pelbagai Mod (Multimodal Sentence Embeddings)

Penyematan zarah pelbagai mod memetakan teks dan imej (dan kadang-kadang audio atau video) ke dalam ruang vektor berterusan yang dikongsi, supaya pasangan yang berkaitan secara semantik dari modaliti yang berbeza mendarat berdekatan. Dilatih dengan objektif kontrastif pada korpus berpasangan yang besar, perwakilan ini menggerakkan pengambilan rentas-modaliti, pengelasan sifar- شوت (zero-shot), dan penaakulan penglihatan-bahasa.

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Sumber

  1. 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. link
  2. Frome, A., Corrado, G. S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., & Mikolov, T. (2013). DeViSE: A deep visual-semantic embedding model. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 26. link

Cara memetik halaman ini

ScholarGate. (2026, June 3). Multimodal Sentence Embeddings (Joint Vision-Language Representation Learning). ScholarGate. https://scholargate.app/ms/deep-learning/multimodal-sentence-embeddings

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ScholarGateMultimodal Sentence Embeddings (Multimodal Sentence Embeddings (Joint Vision-Language Representation Learning)). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/multimodal-sentence-embeddings · Set data: https://doi.org/10.5281/zenodo.20539026