Machine learningDeep learning / NLP / CV
自监督实例分割
自监督实例分割旨在无需任何人工标注的掩码或边界框即可检测和描绘图像中的单个对象实例。它不依赖于昂贵的像素级标签，而是利用自监督预训练、多视图一致性和伪标签生成，纯粹从原始图像数据中发现和分割对象。
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来源
- Wang, X., Zhu, Z., Cao, G., Yao, Z., Jiang, Z., & Ye, J. (2022). FreeSOLO: Learning to Segment Objects without Annotations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14176–14186. link ↗
- Caron, M., Touvron, H., Misra, I., Jégou, 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Self-supervised Instance Segmentation (Label-free Object Mask Learning). ScholarGate. https://scholargate.app/zh/deep-learning/self-supervised-instance-segmentation
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