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.

通过引入一种新颖的方法，即 C-CoTTA，它明确防止任何单个类别侵占其他类别，从而减轻不可控转换引起的各类别之间的相互影响，减少模型对领域转换的敏感性，从而降低类别转变的程度。

可控的持续测试时间自适应

Controllable Continual Test-Time Adaptation

Continual Test Time Adaptation (CTTA) is required to adapt efficiently to
continuous unseen domains while retaining previously learned knowledge.
However, despite the progress of CTTA, forgetting-adaptation trade-offs and
efficiency are still unexplored. Moreover, current CTTA scenarios assume only
the disjoint situation, even though real-world domains are seamlessly changed.
To tackle these challenges, this paper proposes BECoTTA, an input-dependent yet
efficient framework for CTTA. We propose Mixture-of-Domain Low-rank Experts
(MoDE) that contains two core components: i) Domain-Adaptive Routing, which
aids in selectively capturing the domain-adaptive knowledge with multiple
domain routers, and (ii) Domain-Expert Synergy Loss to maximize the dependency
between each domain and expert. We validate our method outperforms multiple
CTTA scenarios including disjoint and gradual domain shits, while only
requiring ~98% fewer trainable parameters. We also provide analyses of our
method, including the construction of experts, the effect of domain-adaptive
experts, and visualizations.

这篇论文提出了 BECoTTA，一个对连续未见域进行输入依赖型的高效适应的框架，其中包含了领域自适应路由和领域专家协同损失等核心组件，并验证了其相对于多种 CTTA 情景的优越性和仅需要 98% 较少可训练参数的需求。