  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, it is still challenging to deploy the
model with improved forgetting-adaptation trade-offs and efficiency. In
addition, current CTTA scenarios assume only the disjoint situation, even
though real-world domains are seamlessly changed. To address these challenges,
this paper proposes BECoTTA, an input-dependent and efficient modular framework
for CTTA. We propose Mixture-of Domain Low-rank Experts (MoDE) that contains
two core components: (i) Domain-Adaptive Routing, which helps to selectively
capture 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 that 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.
