# FuSS: Fusing Superpixels for Improved Segmentation Consistency

Source: [FuSS: Fusing Superpixels for Improved Segmentation Consistency](<https://www.sota2.com/research/paper/68fa6cb31e209131cea3554c>)

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

Data updated: 2025-10-26T00:16:46.056Z

Authors: Ian Nunes, Matheus B. Pereira, Hugo Oliveira, Jefersson A. Dos Santos, Marcus Poggi

Published: 2022-06-06T16:14:19.000Z

## Abstract

In this work, we propose two different approaches to improve the semantic consistency of Open Set Semantic Segmentation. First, we propose a method called OpenGMM that extends the OpenPCS framework using a Gaussian Mixture of Models to model the distribution of pixels for each class in a multimodal manner. The second approach is a post-processing which uses superpixels to enforce highly homogeneous regions to behave equally, rectifying erroneous classified pixels within these regions, we also proposed a novel superpixel method called FuSS. All tests were performed on ISPRS Vaihingen and Potsdam datasets, and both methods were capable to improve quantitative and qualitative results for both datasets. Besides that, the post-process with FuSS achieved state-of-the-art results for both datasets. The official implementation is available at: \\url{https://github.com/iannunes/FuSS}.

## 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/2206.02714>)

[PDF](<https://arxiv.org/pdf/2206.02714.pdf>)