  The field-of-view is an important metric when designing a model for semantic
segmentation. To obtain a large field-of-view, previous approaches generally
choose to rapidly downsample the resolution, usually with average poolings or
stride 2 convolutions. We take a different approach by using dilated
convolutions with large dilation rates throughout the backbone, allowing the
backbone to easily tune its field-of-view by adjusting its dilation rates, and
show that it's competitive with existing approaches. To effectively use the
dilated convolution, we show a simple upper bound on the dilation rate in order
to not leave gaps in between the convolutional weights, and design an
SE-ResNeXt inspired block structure that uses two parallel $3\times 3$
convolutions with different dilation rates to preserve the local details.
Manually tuning the dilation rates for every block can be difficult, so we also
introduce a differentiable neural architecture search method that uses gradient
descent to optimize the dilation rates. In addition, we propose a lightweight
decoder that restores local information better than common alternatives. To
demonstrate the effectiveness of our approach, our model RegSeg achieves
competitive results on real-time Cityscapes and CamVid datasets. Using a T4 GPU
with mixed precision, RegSeg achieves 78.3 mIOU on Cityscapes test set at $37$
FPS, and 80.9 mIOU on CamVid test set at $112$ FPS, both without ImageNet
pretraining.
