论文标题
通过梯度加权类激活映射进行可解释的语义分割
Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping
论文作者
论文摘要
在各种图像识别任务中，卷积神经网络已成为最新的。但是，对他们的预测的解释是一个积极的研究领域。尽管已经提出了各种解释方法进行图像分类，但图像分割的解释仍然在很大程度上没有探索。为此，我们提出了SEG-Grad-CAM，这是一种基于梯度解释语义分割的方法。我们的方法是广泛使用的Grad-CAM方法的扩展，该方法在本地应用，用于生成热图，以显示单个像素在语义分段中的相关性。
Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks. The interpretation of their predictions, however, is an active area of research. Whereas various interpretation methods have been suggested for image classification, the interpretation of image segmentation still remains largely unexplored. To that end, we propose SEG-GRAD-CAM, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.
