Compara mètodes

	Segmentació Explicable d'Instàncies	Segmentació Semàntica Explicable
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2017–present	2019–2021
Autor original	He, K. et al. (Mask R-CNN); XAI extensions by multiple authors	Combination: Long et al. (FCN) + Selvaraju et al. (Grad-CAM); formalized as a unified paradigm ~2019–2021
Tipus	Explainability-augmented deep learning pipeline	Explainable deep learning pipeline
Font seminal	Lindner, M., Meng, C., & Bischl, B. (2023). Explaining Instance Segmentation Models via Saliency Maps and Occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence.	Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626.
Àlies	XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN	XSS, interpretable semantic segmentation, explainable scene parsing, transparent pixel-wise classification
Relacionats	6	4
Resum	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.	Explainable Semantic Segmentation (XSS) couples pixel-wise scene parsing — assigning a class label to every pixel in an image — with post-hoc or intrinsic explanation methods such as Grad-CAM, attention maps, or SHAP, so that the network's class decisions can be audited, visualized, and justified to domain experts in medical imaging, autonomous driving, and remote sensing.