Machine learningDeep learning / NLP / CV
自监督语义分割
自监督语义分割旨在无需手动标注的分割掩码即可为图像中的每个像素分配类别标签。首先，在大量无标签图像上使用对比学习或掩码图像建模等自监督目标训练一个骨干网络，然后利用由此产生的密集特征来分割和标注图像区域，从而以较低的标注成本获得具有竞争力的分割质量。
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来源
- 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. DOI: 10.1109/ICCV48922.2021.00951 ↗
- Hamilton, M., Zhang, Z., Hariharan, B., Snavely, N., & Freeman, W. T. (2022). Unsupervised Semantic Segmentation by Distilling Feature Correspondences. International Conference on Learning Representations (ICLR). link ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Learning for Semantic Segmentation. ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-semantic-segmentation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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