  Recently, numerous handcrafted and searched networks have been applied for
semantic segmentation. However, previous works intend to handle inputs with
various scales in pre-defined static architectures, such as FCN, U-Net, and
DeepLab series. This paper studies a conceptually new method to alleviate the
scale variance in semantic representation, named dynamic routing. The proposed
framework generates data-dependent routes, adapting to the scale distribution
of each image. To this end, a differentiable gating function, called soft
conditional gate, is proposed to select scale transform paths on the fly. In
addition, the computational cost can be further reduced in an end-to-end manner
by giving budget constraints to the gating function. We further relax the
network level routing space to support multi-path propagations and
skip-connections in each forward, bringing substantial network capacity. To
demonstrate the superiority of the dynamic property, we compare with several
static architectures, which can be modeled as special cases in the routing
space. Extensive experiments are conducted on Cityscapes and PASCAL VOC 2012 to
illustrate the effectiveness of the dynamic framework. Code is available at
https://github.com/yanwei-li/DynamicRouting.
