Continual Test-Time Adaptation (CTTA) involves adapting a pre-trained source model to continually changing unsupervised target domains. In this paper, we systematically analyze the challenges of this task: online environment, unsupervised nature, and the risks of error accumulation and catastrophic forgetting under continual domain shifts. To address these challenges, we reshape the online data buffering and organizing mechanism for CTTA. We propose an {uncertainty-aware buffering approach} to identify {and aggregate} significant samples with high certainty from the unsupervised, single-pass data stream. {Based on this}, we propose a graph-based class relation preservation constraint to overcome catastrophic forgetting. Furthermore, a pseudo-target replay objective is used to mitigate error accumulation. Extensive experiments demonstrate the superiority of our method in both segmentation and classification CTTA tasks. Code is available at \href{https://github.com/z1358/OBAO}{this https URL}.

翻译：持续测试时适应（CTTA）旨在将预训练的源模型适应于持续变化的无监督目标域。本文系统分析了该任务面临的挑战：在线环境、无监督特性，以及持续域偏移下错误累积与灾难性遗忘的风险。为应对这些挑战，我们重塑了CTTA的在线数据缓冲与组织机制。我们提出一种基于不确定性的缓冲方法，用于从无监督的单次数据流中识别并聚合具有高确定性的重要样本。在此基础上，我们提出一种基于图的类关系保持约束以克服灾难性遗忘。此外，采用伪目标回放目标来缓解错误累积。大量实验证明了我们的方法在分割与分类CTTA任务中的优越性。代码发布于\href{https://github.com/z1358/OBAO}{此https网址}。