Continual Test-Time Adaptation (CTTA) is an emerging and challenging task where a model trained in a source domain must adapt to continuously changing conditions during testing, without access to the original source data. CTTA is prone to error accumulation due to uncontrollable domain shifts, leading to blurred decision boundaries between categories. Existing CTTA methods primarily focus on suppressing domain shifts, which proves inadequate during the unsupervised test phase. In contrast, we introduce a novel approach that guides rather than suppresses these shifts. Specifically, we propose $\textbf{C}$ontrollable $\textbf{Co}$ntinual $\textbf{T}$est-$\textbf{T}$ime $\textbf{A}$daptation (C-CoTTA), which explicitly prevents any single category from encroaching on others, thereby mitigating the mutual influence between categories caused by uncontrollable shifts. Moreover, our method reduces the sensitivity of model to domain transformations, thereby minimizing the magnitude of category shifts. Extensive quantitative experiments demonstrate the effectiveness of our method, while qualitative analyses, such as t-SNE plots, confirm the theoretical validity of our approach.

翻译：持续测试时适应（CTTA）是一项新兴且具有挑战性的任务，要求一个在源域训练的模型能够在测试阶段适应持续变化的环境，且无法访问原始源数据。由于不可控的域偏移，CTTA容易产生误差累积，导致类别间的决策边界模糊。现有的CTTA方法主要侧重于抑制域偏移，这在无监督测试阶段被证明是不够的。相比之下，我们引入了一种新的方法，旨在引导而非抑制这些偏移。具体而言，我们提出了**可**控**持**续**测**试**时**适**应**（C-CoTTA），该方法明确防止任何单一类别侵占其他类别，从而减轻由不可控偏移引起的类别间相互影响。此外，我们的方法降低了模型对域变换的敏感性，从而最小化了类别偏移的幅度。大量的定量实验证明了我们方法的有效性，而定性分析（如t-SNE图）则证实了我们方法的理论有效性。