Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active learning methods may be less effective since they do not consider the domain shift issue. Despite active DA methods address this by further proposing targetness to measure the representativeness of target domain characteristics, their predictive uncertainty is usually based on the prediction of deterministic models, which can easily be miscalibrated on data with distribution shift. Considering this, we propose a \textit{Dirichlet-based Uncertainty Calibration} (DUC) approach for active DA, which simultaneously achieves the mitigation of miscalibration and the selection of informative target samples. Specifically, we place a Dirichlet prior on the prediction and interpret the prediction as a distribution on the probability simplex, rather than a point estimate like deterministic models. This manner enables us to consider all possible predictions, mitigating the miscalibration of unilateral prediction. Then a two-round selection strategy based on different uncertainty origins is designed to select target samples that are both representative of target domain and conducive to discriminability. Extensive experiments on cross-domain image classification and semantic segmentation validate the superiority of DUC.

翻译：主动域适应旨在通过主动选择少量目标域数据进行标注，从而最大程度提升模型在新目标域上的适应性。然而，传统的主动学习方法因未考虑域偏移问题，其效果可能有限。尽管现有主动域适应方法通过进一步提出“目标性”来衡量目标域特征的典型性来解决这一问题，但其预测不确定性通常基于确定性模型的预测结果，这在数据分布发生偏移时容易导致校准偏差。针对这一问题，我们提出了一种基于狄利克雷的不确定性校准方法（DUC），该方法在缓解校准偏差与选择信息量高的目标样本两方面同时取得成效。具体而言，我们在预测中引入狄利克雷先验，将预测结果解释为概率单纯形上的分布，而非像确定性模型那样采用点估计。这种方式使我们能够考虑所有可能的预测结果，从而缓解单边预测的校准偏差。随后，基于不同不确定性来源，设计了一种两轮选择策略，以选取既具有目标域代表性又有利于判别能力的目标样本。在跨域图像分类和语义分割任务上的大量实验验证了DUC方法的优越性。