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

Source: [Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation](<https://www.sota2.com/research/paper/68fa644d1e209131cea03e01>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:01:12.135Z

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

Published: 2018-04-27T05:03:32.000Z

## 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 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1804.10343>)

[PDF](<https://arxiv.org/pdf/1804.10343.pdf>)