方法对比
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| 可解释实例分割× | 可解释目标检测× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017–present | 2016–2017 |
| 提出者≠ | He, K. et al. (Mask R-CNN); XAI extensions by multiple authors | Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP) |
| 类型≠ | Explainability-augmented deep learning pipeline | Post-hoc explainability applied to object detection |
| 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 | XAI Object Detection, Interpretable Object Detection, Transparent Object Detection, Explainable OD |
| 相关≠ | 6 | 5 |
| 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 object detection combines a deep-learning object detector — such as YOLO, Faster R-CNN, or DETR — with post-hoc or built-in explainability methods (Grad-CAM, LIME, SHAP, D-RISE) that visualize why the model placed a bounding box at a particular location and assigned a particular class label, making its decisions auditable by humans. |
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