Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping
This addresses the need for interpretability in semantic segmentation, which is largely unexplored compared to image classification, but it is incremental as it builds on existing Grad-CAM methods.
The paper tackled the problem of interpreting semantic segmentation predictions by proposing SEG-GRAD-CAM, an extension of Grad-CAM, to generate heatmaps showing pixel relevance for segmentation.
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.