ScholarGate
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Machine learningDeep learning / NLP / CV

Transformeri wa Multimodal

Transformeri wa Multimodal huongeza usanifu wa kawaida wa Transformer ili kuchakata na kuunganisha kwa pamoja zaidi ya aina mbili za pembejeo — kwa kawaida maandishi na picha, lakini pia sauti, video, au data iliyopangwa. Safu za umakini wa pande mbili huruhusu habari kutoka kwa aina moja kuathiri uwakilishi katika nyingine, ikiwezesha majukumu kama vile kujibu maswali ya kuona, kuandika picha, na uchambuzi wa hisia multimodal.

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Vyanzo

  1. Lu, J., Batra, D., Parikh, D., & Lee, S. (2019). ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks. Advances in Neural Information Processing Systems (NeurIPS), 32. link
  2. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021). Learning Transferable Visual Models From Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Multimodal Transformer (Cross-Modal Attention-Based Architecture). ScholarGate. https://scholargate.app/sw/deep-learning/multimodal-transformer

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Imerejelewa na

ScholarGateMultimodal Transformer (Multimodal Transformer (Cross-Modal Attention-Based Architecture)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/multimodal-transformer · Seti ya data: https://doi.org/10.5281/zenodo.20539026