Machine learningDeep learning / NLP / CV

可解释目标检测

可解释目标检测将深度学习目标检测器（如 YOLO、Faster R-CNN 或 DETR）与事后或内置的可解释性方法（Grad-CAM、LIME、SHAP、D-RISE）相结合，可视化模型为何将边界框放置在特定位置并分配特定类别标签，从而使人类能够审计其决策。

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可解释目标检测

可解释图像分类可解释视觉 Transformer 实例分割目标检测语义分割可解释实例分割

来源

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: 10.1109/ICCV.2017.74 ↗
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). 'Why Should I Trust You?': Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. DOI: 10.1145/2939672.2939778 ↗

如何引用本页

ScholarGate. (2026, June 3). Explainable Artificial Intelligence for Object Detection (XAI-OD). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-object-detection

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可解释实例分割

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