手法を比較

	説明可能なVision Transformer	セマンティックセグメンテーション
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2021	2015
提唱者	Chefer, H., Gur, S., & Wolf, L. (attribution framework); Dosovitskiy et al. (base ViT)	Long, J., Shelhamer, E., & Darrell, T.
種類	Post-hoc explainability applied to Vision Transformer	Dense prediction / pixel-wise classification
原典	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.	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.
別名	XViT, Interpretable ViT, Explainable ViT, Transparent Vision Transformer	pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation
関連	5	5
概要	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.	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.