i-WiViG: Interpretable Window Vision GNN
Journal:
arXiv
Published Date:
Mar 11, 2025
Abstract
Deep learning models based on graph neural networks have emerged as a popular
approach for solving computer vision problems. They encode the image into a
graph structure and can be beneficial for efficiently capturing the long-range
dependencies typically present in remote sensing imagery. However, an important
drawback of these methods is their black-box nature which may hamper their
wider usage in critical applications. In this work, we tackle the
self-interpretability of the graph-based vision models by proposing our
Interpretable Window Vision GNN (i-WiViG) approach, which provides explanations
by automatically identifying the relevant subgraphs for the model prediction.
This is achieved with window-based image graph processing that constrains the
node receptive field to a local image region and by using a self-interpretable
graph bottleneck that ranks the importance of the long-range relations between
the image regions. We evaluate our approach to remote sensing classification
and regression tasks, showing it achieves competitive performance while
providing inherent and faithful explanations through the identified relations.
Further, the quantitative evaluation reveals that our model reduces the
infidelity of post-hoc explanations compared to other Vision GNN models,
without sacrificing explanation sparsity.
