  Contextual information is crucial for semantic segmentation. However, finding
the optimal trade-off between keeping desired fine details and at the same time
providing sufficiently large receptive fields is non trivial. This is even more
so, when objects or classes present in an image significantly vary in size.
Dilated convolutions have proven valuable for semantic segmentation, because
they allow to increase the size of the receptive field without sacrificing
image resolution. However, in current state-of-the-art methods, dilation
parameters are hand-tuned and fixed. In this paper, we present an approach for
learning dilation parameters adaptively per channel, consistently improving
semantic segmentation results on street-scene datasets like Cityscapes and
Camvid.
