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自监督视觉Transformer

自监督视觉Transformer（SSL-ViT）将掩码图像块预测（MAE）或无标签自蒸馏（DINO）等自监督预训练目标应用于视觉Transformer架构，从而在任何特定任务的微调之前，从大型无标签图像语料库中学习强大的视觉表示。

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自监督视觉Transformer
微调视觉Transformer多模态视觉变换器自监督卷积神经网络Vision Transformer可解释视觉 Transformer自监督语义分割半监督实例分割半监督视觉变换器

来源

  1. Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660. link ↗
  2. He, K., Chen, X., Xie, S., Li, Y., Dollar, P., & Girshick, R. (2022). Masked Autoencoders Are Scalable Vision Learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16000–16009. link ↗

如何引用本页

ScholarGate. (2026, June 3). Self-supervised Vision Transformer (SSL-ViT). ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-vision-transformer

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被引用于

可解释视觉 Transformer自监督卷积神经网络自监督语义分割半监督实例分割半监督视觉变换器

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ScholarGate — Self-supervised Vision Transformer (Self-supervised Vision Transformer (SSL-ViT)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/self-supervised-vision-transformer · 数据集： https://doi.org/10.5281/zenodo.20539026