The objective of Continual Test-time Domain Adaptation (CTDA) is to gradually adapt a pre-trained model to a sequence of target domains without accessing the source data. This paper proposes a Dynamic Sample Selection (DSS) method for CTDA. DSS consists of dynamic thresholding, positive learning, and negative learning processes. Traditionally, models learn from unlabeled unknown environment data and equally rely on all samples' pseudo-labels to update their parameters through self-training. However, noisy predictions exist in these pseudo-labels, so all samples are not equally trustworthy. Therefore, in our method, a dynamic thresholding module is first designed to select suspected low-quality from high-quality samples. The selected low-quality samples are more likely to be wrongly predicted. Therefore, we apply joint positive and negative learning on both high- and low-quality samples to reduce the risk of using wrong information. We conduct extensive experiments that demonstrate the effectiveness of our proposed method for CTDA in the image domain, outperforming the state-of-the-art results. Furthermore, our approach is also evaluated in the 3D point cloud domain, showcasing its versatility and potential for broader applicability.

翻译：持续测试时域自适应（CTDA）的目标是在不访问源数据的情况下，逐步将预训练模型适应到一系列目标域。本文提出了一种用于CTDA的动态样本选择（DSS）方法。DSS包括动态阈值化、正学习和负学习过程。传统方法中，模型从未标注的未知环境数据中学习，并通过自训练均匀依赖所有样本的伪标签更新参数。然而，这些伪标签中存在噪声预测，因此并非所有样本都同样可靠。为此，我们首先设计了一个动态阈值模块，从高质量样本中筛选出疑似低质量样本。低质量样本更可能被错误预测，因此我们对高质量和低质量样本联合应用正负学习，以降低使用错误信息的风险。我们进行的广泛实验证明了所提方法在图像域CTDA中的有效性，其性能优于现有最佳结果。此外，该方法还在3D点云域中进行了评估，展示了其广泛适用性的潜力。