Machine learningDeep learning / NLP / CV
弱监督图像分类
弱监督图像分类使用仅有的粗粒度、不完整或带噪声的监督信号（例如，图像级类别标签、标签、或网络抓取的标签）来训练卷积或基于Transformer的网络，而无需精确的边界框或像素标注。这极大地降低了标注成本，同时仍能实现大规模高精度视觉识别。
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来源
- Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning Deep Features for Discriminative Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2921–2929. DOI: 10.1109/CVPR.2016.319 ↗
- Mahajan, D., Girshick, R., Ramanathan, V., He, K., Paluri, M., Li, Y., Bharambe, A., & van der Maaten, L. (2018). Exploring the Limits of Weakly Supervised Pretraining. Proceedings of the European Conference on Computer Vision (ECCV), 181–196. DOI: 10.1007/978-3-030-01216-8_12 ↗
如何引用本页
ScholarGate. (2026, June 3). Weakly Supervised Image Classification (WSL-IC). ScholarGate. https://scholargate.app/zh/deep-learning/weakly-supervised-image-classification
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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