The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing the scalability and performance. In this paper, we propose a hard-aware instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality and diversity of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. We further enrich the hard class pseudo-labels with inter-image information through a skillfully designed hard-aware pseudo-label augmentation. Besides, we propose the region-adaptive regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. For the non-pseudo-label region, consistency constraint is also constructed to introduce stronger supervision signals during model optimization. Our method is so concise and efficient that it is easy to be generalized to other UDA methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and Cityscapes to Oxford RobotCar demonstrate the superior performance of our approach compared with the state-of-the-art methods.

翻译：标注训练数据与未标注测试数据之间的差异是当前深度学习模型面临的重要挑战。无监督域适应（UDA）旨在解决此类问题。近期研究表明，自训练是实现UDA的有效途径。然而，现有方法在可扩展性与性能之间难以取得平衡。本文针对语义分割任务中的UDA问题，提出了一种硬感知实例自适应自训练框架。为有效提升伪标签的质量与多样性，我们设计了一种创新性的伪标签生成策略，并引入实例自适应选择器。通过精心设计的硬感知伪标签增强技术，我们进一步利用图像间信息丰富了困难类别的伪标签。此外，我们提出区域自适应正则化方法，用于平滑伪标签区域并锐化非伪标签区域。针对非伪标签区域，我们还构建了一致性约束，以在模型优化过程中引入更强的监督信号。该方法简洁高效，易于推广至其他UDA方法。在GTA5→Cityscapes、SYNTHIA→Cityscapes及Cityscapes→Oxford RobotCar等数据集上的实验表明，与现有最优方法相比，本方法展现出更优越的性能。