# This is not what I imagined: Error Detection for Semantic Segmentation through Visual Dissimilarity Source: [This is not what I imagined: Error Detection for Semantic Segmentation through Visual Dissimilarity]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:46:30.691Z Authors: David Haldimann, Hermann Blum, Roland Siegwart, Cesar Cadena Published: 2019-09-02T11:38:43.000Z ## Abstract There has been a remarkable progress in the accuracy of semantic segmentation due to the capabilities of deep learning. Unfortunately, these methods are not able to generalize much further than the distribution of their training data and fail to handle out-of-distribution classes appropriately. This limits the applicability to autonomous or safety critical systems. We propose a novel method leveraging generative models to detect wrongly segmented or out-of-distribution instances. Conditioned on the predicted semantic segmentation, an RGB image is generated. We then learn a dissimilarity metric that compares the generated image with the original input and detects inconsistencies introduced by the semantic segmentation. We present test cases for outlier and misclassification detection and evaluate our method qualitatively and quantitatively on multiple datasets. ## 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]()