Source-linked AI summary
Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping
Kira Vinogradova, Alexandr Dibrov, Gene Myers
TL;DR
Understanding why CNNs make decisions remains immature, and semantic segmentation lacks established visual interpretation methods. SEG-GRAD-CAM extends Grad-CAM locally to generate pixel-relevance heatmaps, producing reasonable visual explanations on Cityscapes.
Problem
CNN decision-making remains poorly understood, while visual interpretation methods have focused predominantly on classification rather than semantic segmentation.
Method
SEG-GRAD-CAM extends Grad-CAM for semantic segmentation by locally generating heatmaps from selected pixels or regions using convolutional feature maps.
Results
The approach produces reasonable visual explanations for semantic segmentation on the Cityscapes dataset.
Takeaways & Limitations
SEG-GRAD-CAM provides visual explanations of CNN decisions for individual pixels or regions in semantic segmentation.
Takeaways & Limitations
Initial results are promising, but systematic heatmap investigation and quantitative demonstration on a suitable synthetic dataset remain future work.
Abstract
from arXiv · showhide
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.
Introduction
CNNs have improved image understanding performance, but why they succeed remains poorly understood. This work introduces SEG-GRAD-CAM, extending Grad-CAM to explain semantic-segmentation decisions through pixel- or region-level heatmaps.
- CNNs have substantially improved image classification, object detection, and image segmentation, while their decision-making remains poorly understood.
- Heatmaps indicate image regions important to a CNN decision and can reveal reliance on idiosyncratic, non-generalizing training-image details.
- Existing gradient-based heatmap methods, including saliency maps and Grad-CAM, have primarily been developed for image classification.
- SEG-GRAD-CAM extends Grad-CAM to semantic segmentation, producing heatmaps that explain the relevance of individual input pixels or regions.
- The authors present SEG-GRAD-CAM as the first approach specifically extending Grad-CAM to produce visual explanations for CNN-based semantic segmentation.
Method
SEG-GRAD-CAM adapts Grad-CAM to semantic segmentation by computing relevance for selected output pixels, object instances, or the entire image. It retains gradient-weighted feature-map aggregation and ReLU filtering, while also allowing intermediate convolutional layers to be analyzed.
- Grad-CAM: Grad-CAM averages class-logit gradients over feature-map pixels to weight kernels, sums the weighted maps, and applies pixel-wise ReLU.ReLU clips negative values at zero, highlighting areas that positively contribute to the chosen class decision.
- SEG-GRAD-CAM: The method also explores feature maps from intermediate convolutional layers instead of restricting analysis to the last convolutional layer.The original Grad-CAM setup uses selected feature maps from the last convolutional layer of a classification network.
- SEG-GRAD-CAM: SEG-GRAD-CAM replaces the classification logit with a selected segmentation output in the Grad-CAM formulation.Semantic-segmentation networks produce class-specific logits for every image pixel, rather than one class distribution per image.
- SEG-GRAD-CAM: The pixel-index set M can target a single pixel, an object instance, or all image pixels, enabling flexible segmentation heatmaps.M denotes pixels of interest in the output mask.
Experiments
Experiments on Cityscapes use a U-Net to show that SEG-GRAD-CAM produces plausible, locally interpretable heatmaps for semantic segmentation. Bottleneck convolutional layers are generally more informative than decoder layers, while whole-image analysis can highlight informative pixels from another class.
- The approach is demonstrated by training a U-Net for semantic segmentation on the Cityscapes dataset.
- U-Net bottleneck convolutional layers are generally more informative than layers near the decoder’s end.
- SEG-GRAD-CAM produces a plausible heatmap for a selected pixel, mostly highlighting similar pixels of the selected class.The heatmap sums feature maps activated for the whole image, so it can extend beyond the selected pixel’s CNN receptive field.
- For all image pixels and class Sky, SEG-GRAD-CAM most strongly highlights tree pixels from class Nature.These pixels may be highly informative for predicting Sky pixels.
Discussion and Future Work
The initial results for SEG-GRAD-CAM are promising, while future work will systematically investigate its heatmaps, visualization choices, interpretation alternatives, and quantitative evaluation.
- Future Work: Future work will systematically investigate the heatmaps generated by SEG-GRAD-CAM.The authors describe their initial results as promising.
- Future Work: The authors will compare different intermediate feature maps selected for visualization.They aim to compare and reason about how these feature-map choices affect visualization.
- Future Work: They may truncate heatmaps to regions directly relevant to predictions at pixels contained in M.This proposed restriction would limit heatmap extent to prediction-relevant regions.
- Future Work: For a fixed class c, they will compare the weights {αk}k=1 obtained at different locations.The passage identifies location-dependent comparisons of these weights as another future direction.
- Future Work: The authors plan to explore other interpretation approaches and demonstrate SEG-GRAD-CAM’s merits quantitatively using a suitable synthetic dataset.The alternative approaches are referenced to Montavon, Samek, and Müller (2018).