# Detection of False Positive and False Negative Samples in Semantic Segmentation

Source: [Detection of False Positive and False Negative Samples in Semantic Segmentation](<https://www.sota2.com/research/paper/68fa66c61e209131cea12275>)

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

Data updated: 2025-10-25T21:57:21.263Z

Authors: Matthias Rottmann, Kira Maag, Robin Chan, Fabian H\\"uger, Peter Schlicht, Hanno Gottschalk

Published: 2019-12-08T13:04:06.000Z

## Abstract

In recent years, deep learning methods have outperformed other methods in image recognition. This has fostered imagination of potential application of deep learning technology including safety relevant applications like the interpretation of medical images or autonomous driving. The passage from assistance of a human decision maker to ever more automated systems however increases the need to properly handle the failure modes of deep learning modules. In this contribution, we review a set of techniques for the self-monitoring of machine-learning algorithms based on uncertainty quantification. In particular, we apply this to the task of semantic segmentation, where the machine learning algorithm decomposes an image according to semantic categories. We discuss false positive and false negative error modes at instance-level and review techniques for the detection of such errors that have been recently proposed by the authors. We also give an outlook on future research directions.

## 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/1912.03673>)

[PDF](<https://arxiv.org/pdf/1912.03673.pdf>)