Vertaile menetelmiä

	Siirto-oppiminen ja instanssisegmentointi	Semanttinen segmentointi
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2017 (Mask R-CNN); transfer learning paradigm: 2010	2015
Kehittäjä	He, K. et al. (Mask R-CNN); transfer learning framework: Pan & Yang	Long, J., Shelhamer, E., & Darrell, T.
Tyyppi	Transfer learning applied to instance segmentation	Dense prediction / pixel-wise classification
Alkuperäislähde	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.	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.
Rinnakkaisnimet	pretrained instance segmentation, fine-tuned Mask R-CNN, transfer learning for panoptic segmentation, domain-adapted instance segmentation	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Liittyvät	4	5
Tiivistelmä	Transfer learning with instance segmentation reuses a backbone convolutional network pretrained on a large image corpus (typically ImageNet or COCO) as the feature extractor for an instance segmentation model such as Mask R-CNN, then fine-tunes the full pipeline on a smaller target dataset. This approach delivers state-of-the-art per-object mask accuracy with a fraction of the labeled data and compute that training from scratch would require.	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.