পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য ইনস্ট্যান্স সেগমেন্টেশন	Explainable Vision Transformer
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017–present	2021
প্রবর্তক	He, K. et al. (Mask R-CNN); XAI extensions by multiple authors	Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT)
ধরন	Explainability-augmented deep learning pipeline	Post-hoc explainability applied to Vision Transformer
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.	Chefer, H., Gur, S., & Wolf, L. (2021). Transformer interpretability beyond attention visualization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 782–791.
অপর নাম	XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN	XViT, Interpretable ViT, Explainable ViT, Transparent Vision Transformer
সম্পর্কিত	6	5
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 Vision Transformer combines the strong image-recognition performance of Vision Transformers (ViT) with attribution techniques — such as relevance propagation, attention rollout, or gradient-weighted attention — that highlight which image regions drive each prediction. The approach enables researchers and practitioners to audit model decisions and satisfy transparency requirements without sacrificing accuracy.