Equipping predicted segmentation with calibrated uncertainty is essential for safety-critical applications. In this work, we focus on capturing the data-inherent uncertainty (aka aleatoric uncertainty) in segmentation, typically when ambiguities …

Incremental learning of semantic segmentation has emerged as a promising strategy for visual scene interpretation in the openworld setting. However, it remains challenging to acquire novel classes in an online fashion for the segmentation task, …