方法对比
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| 可解释目标检测× | 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≠ | 2016–2017 | 2015 |
| 提出者≠ | Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP) | Long, J., Shelhamer, E., & Darrell, T. |
| 类型≠ | Post-hoc explainability applied to object detection | Dense prediction / pixel-wise classification |
| 开创性文献≠ | 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 ↗ | 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 ↗ |
| 别名 | XAI Object Detection, Interpretable Object Detection, Transparent Object Detection, Explainable OD | pixel-wise classification, scene parsing, dense labeling, semantic scene segmentation |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
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