方法对比
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| 可解释实例分割× | 可解释的语义分割× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–present | 2019–2021 |
| 提出者≠ | 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 |
| 类型≠ | Explainability-augmented deep learning pipeline | Explainable deep learning pipeline |
| 开创性文献≠ | 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 ↗ | 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. DOI ↗ |
| 别名 | XAI instance segmentation, interpretable instance segmentation, transparent mask prediction, explainable Mask R-CNN | XSS, interpretable semantic segmentation, explainable scene parsing, transparent pixel-wise classification |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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. |
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