方法对比
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| 自监督目标检测× | 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≠ | 2019–2021 | 2020–2021 |
| 提出者≠ | He et al. (MoCo); Caron et al. (DINO); Henaff et al. (DetCon) | Sohn et al. (STAC); Liu et al. (Unbiased Teacher) |
| 类型≠ | Self-supervised pre-training + supervised fine-tuning | Semi-supervised learning for detection |
| 开创性文献≠ | He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9729–9738. 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 | SSL object detection, self-supervised detection, unsupervised pre-training for detection, contrastive pre-training for detection | SSOD, semi-supervised detection, pseudo-label object detection, label-efficient object detection |
| 相关≠ | 4 | 6 |
| 摘要≠ | Self-supervised object detection uses unlabeled image data to pre-train a visual backbone through pretext tasks such as contrastive learning or masked image modeling, then fine-tunes the backbone with a detection head on a smaller labeled dataset. This approach dramatically reduces reliance on expensive bounding-box annotations while matching or approaching fully supervised detection performance. | 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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