Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping

Understand

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.