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| 설명 가능한 인스턴스 분할× | 인스턴스 분할× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2017–present | 2017 |
| 창시자≠ | He, K. et al. (Mask R-CNN); XAI extensions by multiple authors | He, K., Gkioxari, G., Dollar, P., Girshick, R. |
| 유형≠ | Explainability-augmented deep learning pipeline | Pixel-level detection and mask prediction |
| 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 ↗ | He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969. DOI ↗ |
| 별칭 | XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN | instance-level segmentation, object instance segmentation, mask prediction, panoptic instance segmentation |
| 관련≠ | 6 | 4 |
| 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. | Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding. |
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