Сравнение на методи

	Semantic Segmentation	Инстанс сегментация
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2015	2017
Създател	Long, J., Shelhamer, E., & Darrell, T.	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Тип	Dense prediction / pixel-wise classification	Pixel-level detection and mask prediction
Основополагащ източник	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.	He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.
Други названия	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Свързани	5	4
Резюме	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.	Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding.