Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation. (arXiv:2203.09744v1 [cs.CV])

Domain adaptive semantic segmentation aims to learn a model with the
supervision of source domain data, and produce satisfactory dense predictions
on unlabeled target domain. One popular solution to this challenging task is
self-training, which selects high-scoring predictions on target samples as
pseudo labels for training. However, the produced pseudo labels often contain
much noise because the model is biased to source domain as well as majority
categories. To address the above issues, we propose to directly explore the
intrinsic …

arxiv cv labeling pixel segmentation semantic