Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation

Sohil Shah, Pallabi Ghosh, Larry S Davis, Tom Goldstein

Abstract: 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 down-sampled 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 net- work 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.

Keywords: semantic segmentation, stacked u-nets, classification

TL;DR: Presents new architecture which leverages information globalization power of u-nets in a deeper networks and performs well across tasks without any bells and whistles.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1804.10343/code)