Σύγκριση μεθόδων

	Επεξηγήσιμη Τμηματοποίηση Αντικειμένων	Σημασιολογική Τμηματοποίηση
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2017–present	2015
Δημιουργός	He, K. et al. (Mask R-CNN); XAI extensions by multiple authors	Long, J., Shelhamer, E., & Darrell, T.
Τύπος	Explainability-augmented deep learning pipeline	Dense prediction / pixel-wise classification
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
Συναφείς	6	5
Σύνοψη	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.