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.

活动领域适应作为解决语义分割中昂贵的标注成本和训练模型性能问题的方法已经出现。然而，现有的研究通常忽视了特征空间中所选样本及其局部上下文之间的关联，从而导致标注预算使用效果较差。本项工作重新审视了经典核心集方法的理论界限，并确认性能与所选样本周围的局部样本分布密切相关。为了有效估计局部样本的密度，我们引入了一种带有动态遮罩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界限。大量实验证明了我们方法的优越性。此外，仅使用极少的标签，我们的方案实现了与完全监督对照组相当的性能。

密度的重要性：活跃领域自适应分割的改进核心集