  Test-Time Adaptation (TTA) aims to adapt pre-trained models to the target
domain during testing. In reality, this adaptability can be influenced by
multiple factors. Researchers have identified various challenging scenarios and
developed diverse methods to address these challenges, such as dealing with
continual domain shifts, mixed domains, and temporally correlated or imbalanced
class distributions. Despite these efforts, a unified and comprehensive
benchmark has yet to be established. To this end, we propose a Unified
Test-Time Adaptation (UniTTA) benchmark, which is comprehensive and widely
applicable. Each scenario within the benchmark is fully described by a Markov
state transition matrix for sampling from the original dataset. The UniTTA
benchmark considers both domain and class as two independent dimensions of data
and addresses various combinations of imbalance/balance and
i.i.d./non-i.i.d./continual conditions, covering a total of \( (2 \times 3)^2 =
36 \) scenarios. It establishes a comprehensive evaluation benchmark for
realistic TTA and provides a guideline for practitioners to select the most
suitable TTA method. Alongside this benchmark, we propose a versatile UniTTA
framework, which includes a Balanced Domain Normalization (BDN) layer and a
COrrelated Feature Adaptation (COFA) method--designed to mitigate distribution
gaps in domain and class, respectively. Extensive experiments demonstrate that
our UniTTA framework excels within the UniTTA benchmark and achieves
state-of-the-art performance on average. Our code is available at
\url{https://github.com/LeapLabTHU/UniTTA}.
