# Semantic Segmentation with Labeling Uncertainty and Class Imbalance Source: [Semantic Segmentation with Labeling Uncertainty and Class Imbalance]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:15:46.115Z Authors: Patrik Ol\\~a Bressan, Jos\\'e Marcato Junior, Jos\\'e Augusto Correa Martins, Diogo Nunes Gon\\c{c}alves, Daniel Matte Freitas, Lucas Prado Osco, Jonathan de Andrade Silva, Zhipeng Luo, Jonathan Li, Raymundo Cordero Garcia, Wesley Nunes Gon\\c{c}alves Published: 2021-02-08T22:53:33.000Z ## Abstract Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks. However, challenges such as the class imbalance and the uncertainty in the pixel-labeling process are not completely addressed. As such, we present a new approach that calculates a weight for each pixel considering its class and uncertainty during the labeling process. The pixel-wise weights are used during training to increase or decrease the importance of the pixels. Experimental results show that the proposed approach leads to significant improvements in three challenging segmentation tasks in comparison to baseline methods. It was also proved to be more invariant to noise. The approach presented here may be used within a wide range of semantic segmentation methods to improve their robustness. ## 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]() [PDF]()