  Recently, there has been a growing interest in developing saliency methods
that provide visual explanations of network predictions. Still, the usability
of existing methods is limited to image classification models. To overcome this
limitation, we extend the existing approaches to generate grid saliencies,
which provide spatially coherent visual explanations for (pixel-level) dense
prediction networks. As the proposed grid saliency allows to spatially
disentangle the object and its context, we specifically explore its potential
to produce context explanations for semantic segmentation networks, discovering
which context most influences the class predictions inside a target object
area. We investigate the effectiveness of grid saliency on a synthetic dataset
with an artificially induced bias between objects and their context as well as
on the real-world Cityscapes dataset using state-of-the-art segmentation
networks. Our results show that grid saliency can be successfully used to
provide easily interpretable context explanations and, moreover, can be
employed for detecting and localizing contextual biases present in the data.
