In this work, we for the first time present a method for detecting label errors in image datasets with semantic segmentation, i.e., pixel-wise class labels. Annotation acquisition for semantic segmentation datasets is time-consuming and requires plenty of human labor. In particular, review processes are time consuming and label errors can easily be overlooked by humans. The consequences are biased benchmarks and in extreme cases also performance degradation of deep neural networks (DNNs) trained on such datasets. DNNs for semantic segmentation yield pixel-wise predictions, which makes detection of label errors via uncertainty quantification a complex task. Uncertainty is particularly pronounced at the transitions between connected components of the prediction. By lifting the consideration of uncertainty to the level of predicted components, we enable the usage of DNNs together with component-level uncertainty quantification for the detection of label errors. We present a principled approach to benchmarking the task of label error detection by dropping labels from the Cityscapes dataset as well from a dataset extracted from the CARLA driving simulator, where in the latter case we have the labels under control. Our experiments show that our approach is able to detect the vast majority of label errors while controlling the number of false label error detections. Furthermore, we apply our method to semantic segmentation datasets frequently used by the computer vision community and present a collection of label errors along with sample statistics.
翻译:在这项工作中,我们第一次提出一种方法,用语义分解来检测图像数据集中的标签差错,即像素类标签。为语义分解数据集获取注释耗时,需要大量人力劳动。特别是,审查进程耗时,标签差错很容易被人类忽略。结果有偏差基准,在极端情况下,受过此类数据集培训的深神经网络(DNN)的性能退化也会产生一种方法。用于语义分解的调解误差的DNNS产生像素预测,这使得通过不确定性量化经常发现标签差错是一项复杂的任务。在预测的连接组成部分之间的过渡中特别明显地表现出不确定性。通过将不确定性的考虑提升到预测组成部分的水平,我们就能够使用DNNS与组成部分级不确定性量化来检测标签差错。我们提出了一个原则性的方法,通过从CARA驱动区段解的数据集中丢弃标签差错错错,从而产生像素的预测结果。在CARA驱动区段中,我们用大量图像分解方法来检测标签错误。我们用大多数的标签来检测。