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| Itseohjautuva instanssisegmentointi× | Semanttinen segmentointi× | |
|---|---|---|
| Tieteenala | Syväoppiminen | Syväoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2021–2022 | 2015 |
| Kehittäjä≠ | Wang et al. (FreeSOLO); Caron et al. (DINO) | Long, J., Shelhamer, E., & Darrell, T. |
| Tyyppi≠ | Self-supervised deep learning for pixel-level object delineation | Dense prediction / pixel-wise classification |
| Alkuperäislähde≠ | Wang, X., Zhu, Z., Cao, G., Yao, Z., Jiang, Z., & Ye, J. (2022). FreeSOLO: Learning to Segment Objects without Annotations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14176–14186. link ↗ | Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3431–3440. DOI ↗ |
| Rinnakkaisnimet | SSIS, unsupervised instance segmentation, label-free instance segmentation, self-supervised mask prediction | pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation |
| Liittyvät≠ | 4 | 5 |
| Tiivistelmä≠ | Self-supervised instance segmentation learns to detect and delineate individual object instances in images without any human-annotated masks or bounding boxes. Instead of relying on costly pixel-level labels, it exploits self-supervised pretraining, multi-view consistency, and pseudo-label generation to discover and segment objects purely from raw image data. | Semantic segmentation assigns a class label to every pixel in an image, producing a dense, category-annotated map of the scene. Unlike object detection, which draws bounding boxes, it delineates the exact spatial extent of each class, making it indispensable in medical imaging, autonomous driving, satellite analysis, and any task where precise region boundaries matter. |
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