  Semantic segmentation is a challenging task that needs to handle large scale
variations, deformations and different viewpoints. In this paper, we develop a
novel network named Gated Path Selection Network (GPSNet), which aims to learn
adaptive receptive fields. In GPSNet, we first design a two-dimensional
multi-scale network - SuperNet, which densely incorporates features from
growing receptive fields. To dynamically select desirable semantic context, a
gate prediction module is further introduced. In contrast to previous works
that focus on optimizing sample positions on the regular grids, GPSNet can
adaptively capture free form dense semantic contexts. The derived adaptive
receptive fields are data-dependent, and are flexible that can model different
object geometric transformations. On two representative semantic segmentation
datasets, i.e., Cityscapes, and ADE20K, we show that the proposed approach
consistently outperforms previous methods and achieves competitive performance
without bells and whistles.
