Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training

Yang Zou, Zhiding Yu, Vijayakumar Bhagavatula, Jinsong Wang

Abstract

Recent deep networks achieved state of the art performance on a variety of semantic segmentation tasks. Despite such progress, these models often face challenges in real world “wild tasks” where large difference between labeled training/source data and unseen test/target data exists. In particular, such difference is often referred to as “domain gap”, and could cause significantly decreased performance which cannot be easily remedied by further increasing the representation power. Unsupervised domain adaptation (UDA) seeks to overcome such problem without target domain labels. In this paper, we propose a novel UDA framework class-balanced self-training procedure.