Leveraging Labeling Representations in Uncertainty-based Semi-supervised Segmentation. (arXiv:2203.05682v1 [cs.CV])

Semi-supervised segmentation tackles the scarcity of annotations by
leveraging unlabeled data with a small amount of labeled data. A prominent way
to utilize the unlabeled data is by consistency training which commonly uses a
teacher-student network, where a teacher guides a student segmentation. The
predictions of unlabeled data are not reliable, therefore, uncertainty-aware
methods have been proposed to gradually learn from meaningful and reliable
predictions. Uncertainty estimation, however, relies on multiple inferences
from model predictions that need to be computed …

arxiv cv labeling segmentation semi-supervised uncertainty