পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য ইনস্ট্যান্স সেগমেন্টেশন	ব্যাখ্যাযোগ্য শব্দার্থিক বিভাজন
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017–present	2019–2021
প্রবর্তক	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
ধরন	Explainability-augmented deep learning pipeline	Explainable deep learning pipeline
মৌলিক উৎস	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.
অপর নাম	XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN	XSS, interpretable semantic segmentation, explainable scene parsing, transparent pixel-wise classification
সম্পর্কিত	6	4
সারসংক্ষেপ	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.