ScholarGate
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Machine learningDeep learning / NLP / CV

Multimodal BERT-baseret klassifikation

Multimodal BERT-baseret klassifikation udvider BERT-transformerarkitekturen til i fællesskab at kode og klassificere data fra flere modaliteter – oftest tekst parret med billeder – ved at fusionere deres repræsentationer før et endeligt klassifikationshoved. Introduceret prominent omkring 2019 gennem modeller som MMBT og ViLBERT, er det blevet en standardtilgang til opgaver, hvor hverken tekst eller billede alene indeholder tilstrækkelig information til nøjagtig mærkning.

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Kilder

  1. Kiela, D., Bhooshan, S., Firooz, H., Perez, E., & Testuggine, D. (2019). Supervised multimodal bitransformers for classifying images and text. arXiv preprint arXiv:1909.02950. link
  2. 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, 32. link

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ScholarGate. (2026, June 3). Multimodal BERT-based Classification (Transformer Fusion of Text and Non-text Modalities). ScholarGate. https://scholargate.app/da/deep-learning/multimodal-bert-based-classification

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ScholarGateMultimodal BERT-based Classification (Multimodal BERT-based Classification (Transformer Fusion of Text and Non-text Modalities)). Hentet 2026-06-15 fra https://scholargate.app/da/deep-learning/multimodal-bert-based-classification · Datasæt: https://doi.org/10.5281/zenodo.20539026