Bandingkan metode

	Segmentasi Instans yang Dapat Dijelaskan	Semantic Segmentation
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2017–present	2015
Pencetus	He, K. et al. (Mask R-CNN); XAI extensions by multiple authors	Long, J., Shelhamer, E., & Darrell, T.
Tipe	Explainability-augmented deep learning pipeline	Dense prediction / pixel-wise classification
Sumber perintis	Lindner, M., Meng, C., & Bischl, B. (2023). Explaining Instance Segmentation Models via Saliency Maps and Occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence.	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.
Alias	XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Terkait	6	5
Ringkasan	Explainable Instance Segmentation combines deep-learning instance segmentation models — which detect and delineate every individual object as a separate pixel mask — with post-hoc or ante-hoc explainability techniques such as GradCAM, SHAP, LIME, or attention visualization, so that each predicted mask is accompanied by evidence showing which image regions drove the model's decision.	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.