Machine learningDeep learning / NLP / CV
弱监督语义分割
弱监督语义分割（WSSS）使用廉价、粗粒度的标注——通常是图像级类别标签——来训练像素级场景解析器，而不是昂贵的密集像素掩码。通过从分类网络生成代理伪标签（通过类激活图或类似的定位线索）并进行迭代优化，WSSS能够以较低的标注成本达到全监督的准确度。
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来源
- Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921–2929. DOI: 10.1109/CVPR.2016.319 ↗
- Ahn, J., & Kwak, S. (2018). Learning Pixel-Wise Semantic Affinity with Image-Level Supervision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4109–4118. link ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Semantic Segmentation (WSSS). ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-semantic-segmentation
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