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:
this https URL

在语义分割任务中，采用空洞卷积作为增加感受野的方法，本研究通过在模型浅层引入大核心空洞卷积以及预训练图像编码器等策略，提出了 DSNet，一种新的双分支卷积神经网络结构，它在 ADE20K、Cityscapes 和 BDD 数据集上以更高的准确率和速度获得了最新的表现。

DSNet：一种在语义分割中使用空洞卷积的新方法

DSNet: A Novel Way to Use Atrous Convolutions in Semantic Segmentation

Convolutional Neural Networks have been the backbone of recent rapid progress
in Single-Image Super-Resolution. However, existing networks are very deep with
many network parameters, thus having a large memory footprint and being
challenging to train. We propose Large Receptive Field Networks which strive to
directly expand the receptive field of Super-Resolution networks without
increasing depth or parameter count. In particular, we use two different
methods to expand the network receptive field: 1-D separable kernels and atrous
convolutions. We conduct considerable experiments to study the performance of
various arrangement schemes of the 1-D separable kernels and atrous convolution
in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing
on the more challenging high upscaling factors. Extensive benchmark evaluations
demonstrate the effectiveness of our approach.

提出了 Large Receptive Field Networks，它可以扩展超分辨率网络的感受野，同时不增加深度或参数计数，通过实验证明该方法在提高准确率（PSNR / SSIM），减少参数计数和提高速度方面具有有效性。