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| 설명 가능한 인스턴스 분할× | Semantic segmentation× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–present | 2015 |
| 창시자≠ | He, K. et al. (Mask R-CNN); XAI extensions by multiple authors | Long, J., Shelhamer, E., & Darrell, T. |
| 유형≠ | Explainability-augmented deep learning pipeline | Dense prediction / pixel-wise classification |
| 원전≠ | Lindner, M., Meng, C., & Bischl, B. (2023). Explaining Instance Segmentation Models via Saliency Maps and Occlusion. IEEE Transactions on Pattern Analysis and Machine Intelligence. link ↗ | 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. DOI ↗ |
| 별칭 | XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN | pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. |
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