方法对比
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| 弱监督目标检测× | 半监督目标检测× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2016 (deep WSOD); MIL roots circa 1997 | 2020–2021 |
| 提出者≠ | Bilen, H. & Vedaldi, A. (WSDDN); Multiple Instance Learning origins: Dietterich et al. (1997) | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) |
| 类型≠ | Weakly supervised detection paradigm | Semi-supervised learning for detection |
| 开创性文献≠ | Bilen, H., & Vedaldi, A. (2016). Weakly supervised deep detection networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2846–2854. DOI ↗ | Sohn, K., Zhang, Z., Li, C.-L., Zhang, H., Lee, C.-Y., & Pfister, T. (2020). A Simple Semi-Supervised Learning Framework for Object Detection. arXiv preprint arXiv:2005.04757. link ↗ |
| 别名 | WSOD, weakly-supervised detection, image-level supervised detection, multiple instance detection | SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection |
| 相关≠ | 5 | 6 |
| 摘要≠ | Weakly Supervised Object Detection (WSOD) trains object detectors using only image-level labels — indicating which object classes appear in an image — without requiring costly bounding-box annotations. Multiple Instance Learning (MIL) formulations allow the model to discover the likely location of each object class from classification signals alone, dramatically reducing annotation cost. | Semi-supervised object detection trains a detector on a small labeled image set and a large unlabeled image set. A teacher model generates pseudo-labels for unlabeled images, and a student model learns from both real and pseudo-labeled data, dramatically reducing the expensive manual bounding-box annotation burden while achieving accuracy competitive with fully supervised baselines. |
| ScholarGate数据集 ↗ |
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