Estimating label quality and errors in semantic segmentation data via
  any model
Vedang Lad Jonas Mueller
The labor-intensive annotation process of semantic segmentation datasets is
often prone to errors, since humans struggle to label every pixel correctly. We
study algorithms to automatically detect such annotation errors, in particular
methods to score label quality, such that the images with the lowest scores are
least likely to be correctly labeled. This helps prioritize what data to review
in order to ensure a high-quality training/evaluation dataset, which is
critical in sensitive applications such as medical imaging and autonomous
vehicles. Widely applicable, our label quality scores rely on probabilistic
predictions from a trained segmentation model -- any model architecture and
training procedure can be utilized. Here we study 7 different label quality
scoring methods used in conjunction with a DeepLabV3+ or a FPN segmentation
model to detect annotation errors in a version of the SYNTHIA dataset.
Precision-recall evaluations reveal a score -- the soft-minimum of the
model-estimated likelihoods of each pixel's annotated class -- that is
particularly effective to identify images that are mislabeled, across multiple
types of annotation error.