Machine learningDeep learning / NLP / CV
可解释的语义分割
可解释的语义分割（XSS）将像素级场景解析——为图像中的每个像素分配一个类别标签——与事后或内在的解释方法（如 Grad-CAM、注意力图或 SHAP）相结合，以便可以对网络的类别决策进行审计、可视化和向医学影像、自动驾驶和遥感领域的领域专家进行论证。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
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 ↗
- Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440. DOI: 10.1109/CVPR.2015.7298965 ↗
如何引用本页
ScholarGate. (2026, June 3). Explainable Semantic Segmentation (XAI-Integrated Pixel-Wise Scene Parsing). ScholarGate. https://scholargate.app/zh/deep-learning/explainable-semantic-segmentation
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
Compare side by side →