方法对比
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| 半监督实例分割× | 弱监督实例分割× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2018–2021 | 2015–2019 |
| 提出者≠ | Multiple independent research groups (2018–2021) | Multiple contributors (e.g., Hsu et al., Khoreva et al.) |
| 类型≠ | Semi-supervised deep learning for dense prediction | Weakly supervised deep learning for pixel-wise instance delineation |
| 开创性文献≠ | Hu, H., Wei, P., Zheng, H., Bai, X., Wei, Y., & Chen, Y. (2021). Semi-supervised Semantic Segmentation via Adaptive Equalization Learning. Advances in Neural Information Processing Systems (NeurIPS), 34, 22106–22118. link ↗ | 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 ↗ |
| 别名 | Semi-supervised Mask R-CNN, pseudo-label instance segmentation, label-efficient instance segmentation, SSIS | WSIS, weakly-supervised mask prediction, weak-label instance segmentation, box-supervised instance segmentation |
| 相关 | 6 | 6 |
| 摘要≠ | Semi-supervised instance segmentation trains a model to detect and delineate every object instance in an image using a small labeled set and a large unlabeled image corpus. By generating pseudo-labels from confident predictions on unlabeled images and enforcing consistency under augmentation, the approach achieves competitive mask accuracy at a fraction of the full annotation cost. | Weakly supervised instance segmentation trains deep networks to delineate individual object instances at pixel level using only cheap, incomplete annotations — such as bounding boxes, image-level labels, or point clicks — rather than costly full pixel-wise masks. It dramatically reduces annotation effort while still producing instance-level masks for each object in an image. |
| ScholarGate数据集 ↗ |
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