To circumvent the costly pixel-wise annotations of real-world images in the
semantic segmentation task, the Unsupervised Domain Adaptation (UDA) is
explored to firstly train a model with the labeled source data (synthetic
images) and then adapt it to the unlabeled target data (real images). Among all
the techniques being studied, the self-training approach recently secures its
position in domain adaptive semantic segmentation, where a model is trained
with target domain pseudo-labels. Current advances have mitigated noisy
pseudo-labels resulting from the domain gap. However, they still struggle with
erroneous pseudo-labels near the decision boundaries of the semantic
classifier. In this paper, we tackle this issue by proposing a dual-level
interaction for domain adaptation (DIDA) in semantic segmentation. Explicitly,
we encourage the different augmented views of the same pixel to have not only
similar class prediction (semantic-level) but also akin similarity relationship
respected to other pixels (instance-level). As it is impossible to keep
features of all pixel instances for a dataset, we novelly design and maintain a
labeled instance bank with dynamic updating strategies to selectively store the
informative features of instances. Further, DIDA performs cross-level
interaction with scattering and gathering techniques to regenerate more
reliable pseudolabels. Our method outperforms the state-of-the-art by a notable
margin, especially on confusing and long-tailed classes. Code is available at
this https URL

本文提出了一个双层互动的域自适应 (DIDA) 方法来解决语义分割伪标签错误的问题，该方法基于无监督域自适应，同时使用实例层面和语义层面相似度实现更可靠的伪标签，实现了优于现有技术的效果。