The Wide Residual Networks (Wide-ResNets), a shallow but wide model variant
of the Residual Networks (ResNets) by stacking a small number of residual
blocks with large channel sizes, have demonstrated outstanding performance on
multiple dense prediction tasks. However, since proposed, the Wide-ResNet
architecture has barely evolved over the years. In this work, we revisit its
architecture design for the recent challenging panoptic segmentation task,
which aims to unify semantic segmentation and instance segmentation. A baseline
model is obtained by incorporating the simple and effective
Squeeze-and-Excitation and Switchable Atrous Convolution to the Wide-ResNets.
Its network capacity is further scaled up or down by adjusting the width (i.e.,
channel size) and depth (i.e., number of layers), resulting in a family of
SWideRNets (short for Scaling Wide Residual Networks). We demonstrate that such
a simple scaling scheme, coupled with grid search, identifies several
SWideRNets that significantly advance state-of-the-art performance on panoptic
segmentation datasets in both the fast model regime and strong model regime.

本文重新设计 Wide-ResNets 的架构用于最新的全景分割任务，通过简单且有效的 Squeeze-and-Excitation 和 Switchable Atrous Convolution 结合调整网络的宽度和深度，形成了一系列 SWideRNets 并通过网格搜索得到了多种在快速和强大模型方案下都可以显著提高全景分割数据集状态的 SWideRNets。

用于全景分割的宽残差网络的扩展

Scaling Wide Residual Networks for Panoptic Segmentation

Many modern object detectors demonstrate outstanding performances by using
the mechanism of looking and thinking twice. In this paper, we explore this
mechanism in the backbone design for object detection. At the macro level, we
propose Recursive Feature Pyramid, which incorporates extra feedback
connections from Feature Pyramid Networks into the bottom-up backbone layers.
At the micro level, we propose Switchable Atrous Convolution, which convolves
the features with different atrous rates and gathers the results using switch
functions. Combining them results in DetectoRS, which significantly improves
the performances of object detection. On COCO test-dev, DetectoRS achieves
state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance
segmentation, and 50.0% PQ for panoptic segmentation. The code is made publicly
available.

本文提出了在物体检测中使用深度神经网络的新的骨干设计 —— 递归特征金字塔和可切换空洞卷积，在 COCO test-dev 数据集上实现了目前最先进的表现。