Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Полу-наблюдавано семантично сегментиране× | Слабо контролирана семантична сегментация× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2018–2020 | 2014–2016 |
| Създател≠ | Multiple (Ouali et al., Zou et al., Chen et al.) | Multiple contributors; Class Activation Mapping (Zhou et al., 2016) is foundational |
| Тип≠ | Semi-supervised deep learning for pixel-level classification | Pixel-level classification with image-level or coarse supervision |
| Основополагащ източник≠ | Ouali, Y., Hudelot, C., & Tami, M. (2020). Semi-Supervised Semantic Segmentation with Cross-Consistency Training. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12674–12684. DOI ↗ | 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 ↗ |
| Други названия | Semi-SSL segmentation, pseudo-label segmentation, consistency regularization segmentation, label-efficient semantic segmentation | WSSS, weak-label segmentation, image-level supervised segmentation, weakly-labeled pixel classification |
| Свързани≠ | 5 | 4 |
| Резюме≠ | Semi-supervised semantic segmentation trains pixel-level labeling models using a small set of fully labeled images combined with a much larger set of unlabeled images. Techniques such as pseudo-labeling and consistency regularization extract supervisory signal from unlabeled data, making it possible to achieve near-fully-supervised accuracy at a fraction of the annotation cost. | Weakly Supervised Semantic Segmentation (WSSS) trains pixel-level scene parsers using only cheap, coarse annotations — typically image-level class tags — instead of costly dense pixel masks. By generating proxy pseudo-labels from a classification network (via Class Activation Maps or similar localisation cues) and iteratively refining them, WSSS brings full-supervision accuracy within reach at a fraction of the annotation cost. |
| ScholarGateНабор от данни ↗ |
|
|