方法对比
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| 可解释目标检测× | 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≠ | 2016–2017 | 2014–2016 |
| 提出者≠ | Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME); Lundberg & Lee (SHAP) | Girshick, R. et al. (R-CNN); Redmon, J. et al. (YOLO) |
| 类型≠ | Post-hoc explainability applied to object detection | Supervised deep learning (region proposal or single-shot) |
| 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 ↗ | Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 580–587. DOI ↗ |
| 别名 | XAI Object Detection, Interpretable Object Detection, Transparent Object Detection, Explainable OD | visual object detection, image object localization, region-based object detection, bounding-box detection |
| 相关≠ | 5 | 3 |
| 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. | Object detection is a computer vision task in which a deep neural network simultaneously locates and classifies every instance of one or more object categories within an image, producing a bounding box and a class label for each detected object. Modern detectors — from the R-CNN family to YOLO and DETR — achieve near-human accuracy at real-time speeds on standard benchmarks. |
| ScholarGate数据集 ↗ |
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