Unsupervised domain adaption has proven to be an effective approach for
alleviating the intensive workload of manual annotation by aligning the
synthetic source-domain data and the real-world target-domain samples.
Unfortunately, mapping the target-domain distribution to the source-domain
unconditionally may distort the essential structural information of the
target-domain data. To this end, we firstly propose to introduce a novel
multi-anchor based active learning strategy to assist domain adaptation
regarding the semantic segmentation task. By innovatively adopting multiple
anchors instead of a single centroid, the source domain can be better
characterized as a multimodal distribution, thus more representative and
complimentary samples are selected from the target domain. With little workload
to manually annotate these active samples, the distortion of the target-domain
distribution can be effectively alleviated, resulting in a large performance
gain. The multi-anchor strategy is additionally employed to model the
target-distribution. By regularizing the latent representation of the target
samples compact around multiple anchors through a novel soft alignment loss,
more precise segmentation can be achieved. Extensive experiments are conducted
on public datasets to demonstrate that the proposed approach outperforms
state-of-the-art methods significantly, along with thorough ablation study to
verify the effectiveness of each component.

该研究提出了一种多锚点主动学习策略用于语义分割无监督领域适应，以缓解手动注释的工作量，并在公共数据集上大量提高性能。