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：连续测试时域自适应（CTDA）的目标是在不访问源数据的情况下，将预训练模型逐步适应于一系列目标域。本文提出了一种面向CTDA的动态样本选择（DSS）方法，该方法由动态阈值化、正向学习和负向学习三个过程组成。传统方法通过自训练从无标签的未知环境数据中学习，依赖所有样本的伪标签更新模型参数。然而，由于伪标签中存在噪声预测，并非所有样本都同等可靠。因此，在我们的方法中，首先设计动态阈值模块，从高质量样本中筛选出疑似低质量样本。被选中的低质量样本更可能存在错误预测。为此，我们对高质量与低质量样本联合应用正向和负向学习，以降低使用错误信息带来的风险。大量实验表明，所提方法在图像域的CTDA任务中优于现有最优结果。此外，该方法在三维点云域上也进行了评估，展现了其广泛适用性的潜力。