Learning semantic segmentation models requires a huge amount of pixel-wise
labeling. However, labeled data may only be available abundantly in a domain
different from the desired target domain, which only has minimal or no
annotations. In this work, we propose a novel framework for domain adaptation
in semantic segmentation with image-level weak labels in the target domain. The
weak labels may be obtained based on a model prediction for unsupervised domain
adaptation (UDA), or from a human annotator in a new weakly-supervised domain
adaptation (WDA) paradigm for semantic segmentation. Using weak labels is both
practical and useful, since (i) collecting image-level target annotations is
comparably cheap in WDA and incurs no cost in UDA, and (ii) it opens the
opportunity for category-wise domain alignment. Our framework uses weak labels
to enable the interplay between feature alignment and pseudo-labeling,
improving both in the process of domain adaptation. Specifically, we develop a
weak-label classification module to enforce the network to attend to certain
categories, and then use such training signals to guide the proposed
category-wise alignment method. In experiments, we show considerable
improvements with respect to the existing state-of-the-arts in UDA and present
a new benchmark in the WDA setting. Project page is at
this http URL

论文提出一个新框架，使用 image-level weak labels，引入了 category-wise alignment 来实现 domain adaptation 中 feature alignment 和 pseudo-labeling 的相互作用，实验结果表明在 UDA 和 WDA 上都有显著的提高。