Instance-wise Uncertainty for Class Imbalance in Semantic Segmentation
Instance-wise Uncertainty for Class Imbalance in Semantic Segmentation
Semantic segmentation is a fundamental computer vision task with a vast number of applications. State of the art methods increasingly rely on deep learning models, known to incorrectly estimate uncertainty and being overconfident in predictions, especially in data not seen during training. This is particularly problematic in semantic segmentation due …