  Atrous convolutions are employed as a method to increase the receptive field
in semantic segmentation tasks. However, in previous works of semantic
segmentation, it was rarely employed in the shallow layers of the model. We
revisit the design of atrous convolutions in modern convolutional neural
networks (CNNs), and demonstrate that the concept of using large kernels to
apply atrous convolutions could be a more powerful paradigm. We propose three
guidelines to apply atrous convolutions more efficiently. Following these
guidelines, we propose DSNet, a Dual-Branch CNN architecture, which
incorporates atrous convolutions in the shallow layers of the model
architecture, as well as pretraining the nearly entire encoder on ImageNet to
achieve better performance. To demonstrate the effectiveness of our approach,
our models achieve a new state-of-the-art trade-off between accuracy and speed
on ADE20K, Cityscapes and BDD datasets. Specifically, DSNet achieves 40.0% mIOU
with inference speed of 179.2 FPS on ADE20K, and 80.4% mIOU with speed of 81.9
FPS on Cityscapes. Source code and models are available at Github:
https://github.com/takaniwa/DSNet.
