  Many imaging tasks require global information about all pixels in an image.
Conventional bottom-up classification networks globalize information by
decreasing resolution; features are pooled and downsampled into a single
output. But for semantic segmentation and object detection tasks, a network
must provide higher-resolution pixel-level outputs. To globalize information
while preserving resolution, many researchers propose the inclusion of
sophisticated auxiliary blocks, but these come at the cost of a considerable
increase in network size and computational cost. This paper proposes stacked
u-nets (SUNets), which iteratively combine features from different resolution
scales while maintaining resolution. SUNets leverage the information
globalization power of u-nets in a deeper network architectures that is capable
of handling the complexity of natural images. SUNets perform extremely well on
semantic segmentation tasks using a small number of parameters.
