Bandingkan metode

	Semantic Segmentation	Segmentasi Instans
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2015	2017
Pencetus	Long, J., Shelhamer, E., & Darrell, T.	He, K., Gkioxari, G., Dollar, P., Girshick, R.
Tipe	Dense prediction / pixel-wise classification	Pixel-level detection and mask prediction
Sumber perintis	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.
Alias	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation	instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation
Terkait	5	4
Ringkasan	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.