  Data-driven based approaches, in spite of great success in many tasks, have
poor generalization when applied to unseen image domains, and require expensive
cost of annotation especially for dense pixel prediction tasks such as semantic
segmentation. Recently, both unsupervised domain adaptation (UDA) from large
amounts of synthetic data and semi-supervised learning (SSL) with small set of
labeled data have been studied to alleviate this issue. However, there is still
a large gap on performance compared to their supervised counterparts. We focus
on a more practical setting of semi-supervised domain adaptation (SSDA) where
both a small set of labeled target data and large amounts of labeled source
data are available. To address the task of SSDA, a novel framework based on
dual-level domain mixing is proposed. The proposed framework consists of three
stages. First, two kinds of data mixing methods are proposed to reduce domain
gap in both region-level and sample-level respectively. We can obtain two
complementary domain-mixed teachers based on dual-level mixed data from
holistic and partial views respectively. Then, a student model is learned by
distilling knowledge from these two teachers. Finally, pseudo labels of
unlabeled data are generated in a self-training manner for another few rounds
of teachers training. Extensive experimental results have demonstrated the
effectiveness of our proposed framework on synthetic-to-real semantic
segmentation benchmarks.
