  Continual Test-Time Adaptation (CTTA) aims to adapt a pre-trained model to a
sequence of target domains during the test phase without accessing the source
data. To adapt to unlabeled data from unknown domains, existing methods rely on
constructing pseudo-labels for all samples and updating the model through
self-training. However, these pseudo-labels often involve noise, leading to
insufficient adaptation. To improve the quality of pseudo-labels, we propose a
pseudo-label selection method for CTTA, called Pseudo Labeling Filter (PLF).
The key idea of PLF is to keep selecting appropriate thresholds for
pseudo-labels and identify reliable ones for self-training. Specifically, we
present three principles for setting thresholds during continuous domain
learning, including initialization, growth and diversity. Based on these
principles, we design Self-Adaptive Thresholding to filter pseudo-labels.
Additionally, we introduce a Class Prior Alignment (CPA) method to encourage
the model to make diverse predictions for unknown domain samples. Through
extensive experiments, PLF outperforms current state-of-the-art methods,
proving its effectiveness in CTTA.
