  Semantic segmentation architectures are mainly built upon an encoder-decoder
structure. These models perform subsequent downsampling operations in the
encoder. Since operations on high-resolution activation maps are
computationally expensive, usually the decoder produces output segmentation
maps by upsampling with parameters-free operators like bilinear or
nearest-neighbor. We propose a Neural Network named Guided Upsampling Network
which consists of a multiresolution architecture that jointly exploits
high-resolution and large context information. Then we introduce a new module
named Guided Upsampling Module (GUM) that enriches upsampling operators by
introducing a learnable transformation for semantic maps. It can be plugged
into any existing encoder-decoder architecture with little modifications and
low additional computation cost. We show with quantitative and qualitative
experiments how our network benefits from the use of GUM module. A
comprehensive set of experiments on the publicly available Cityscapes dataset
demonstrates that Guided Upsampling Network can efficiently process
high-resolution images in real-time while attaining state-of-the art
performances.
