Active domain adaptation has emerged as a solution to balance the expensive annotation cost and the performance of trained models in semantic segmentation. However, existing works usually ignore the correlation between selected samples and its local context in feature space, which leads to inferior usage of annotation budgets. In this work, we revisit the theoretical bound of the classical Core-set method and identify that the performance is closely related to the local sample distribution around selected samples. To estimate the density of local samples efficiently, we introduce a local proxy estimator with Dynamic Masked Convolution and develop a Density-aware Greedy algorithm to optimize the bound. Extensive experiments demonstrate the superiority of our approach. Moreover, with very few labels, our scheme achieves comparable performance to the fully supervised counterpart.

翻译：主动域适应作为一种解决方案，在语义分割中平衡了昂贵的标注成本与训练模型性能之间的矛盾。然而，现有工作通常忽略所选样本与其在特征空间中局部上下文之间的相关性，导致标注预算利用效率低下。本文重新审视了经典核心集方法的理论界限，发现其性能与所选样本周围的局部样本分布密切相关。为高效估计局部样本密度，我们引入了一种基于动态掩码卷积的局部代理估计器，并开发了一种密度感知贪心算法来优化该界限。大量实验证明了我们方法的优越性。此外，在极少量标注下，我们的方案达到了与全监督方法相当的性能。