ScholarGate
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Machine learningDeep learning / NLP / CV

Fine-Tuned Vision Transformer

Fine-Tuned Vision Transformer menyesuaikan model ViT pra-latih berskala besar — yang membahagikan imej kepada tampalan bersaiz tetap dan memprosesnya melalui lapisan perhatian kendiri — kepada tugas klasifikasi atau pengecaman imej baharu menggunakan set data berlabel yang agak kecil. Ia mencapai ketepatan terkini dalam penglihatan komputer dengan memanfaatkan perwakilan kaya yang dipelajari semasa pra-latihan skala besar.

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Sumber

  1. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations (ICLR 2021). link
  2. Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022). Scaling Vision Transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022), pp. 12104-12113. link

Cara memetik halaman ini

ScholarGate. (2026, June 3). Fine-Tuned Vision Transformer (ViT with Task-Specific Adaptation). ScholarGate. https://scholargate.app/ms/deep-learning/fine-tuned-vision-transformer

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ScholarGateFine-Tuned Vision Transformer (Fine-Tuned Vision Transformer (ViT with Task-Specific Adaptation)). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/fine-tuned-vision-transformer · Set data: https://doi.org/10.5281/zenodo.20539026