  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.
