Machine learningDeep learning / NLP / CV
弱监督实例分割
弱监督实例分割训练深度网络，仅使用廉价、不完整的标注（如边界框、图像级标签或点点击）来像素级地描绘单个对象实例，而非昂贵的像素级掩码。它极大地减少了标注工作量，同时仍能为图像中的每个对象生成实例级掩码。
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Method map
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来源
- Hsu, C.-C., Hsu, K.-J., Tsai, C.-C., Lin, Y.-Y., & Chuang, Y.-Y. (2019). Weakly supervised instance segmentation using the bounding box tightness prior. Advances in Neural Information Processing Systems (NeurIPS), 32. link ↗
- Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929. DOI: 10.1109/CVPR.2016.319 ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Instance Segmentation (Deep Learning with Incomplete Annotations). ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-instance-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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