  The objective of Continual Test-time Domain Adaptation (CTDA) is to gradually
adapt a pre-trained model to a sequence of target domains without accessing the
source data. This paper proposes a Dynamic Sample Selection (DSS) method for
CTDA. DSS consists of dynamic thresholding, positive learning, and negative
learning processes. Traditionally, models learn from unlabeled unknown
environment data and equally rely on all samples' pseudo-labels to update their
parameters through self-training. However, noisy predictions exist in these
pseudo-labels, so all samples are not equally trustworthy. Therefore, in our
method, a dynamic thresholding module is first designed to select suspected
low-quality from high-quality samples. The selected low-quality samples are
more likely to be wrongly predicted. Therefore, we apply joint positive and
negative learning on both high- and low-quality samples to reduce the risk of
using wrong information. We conduct extensive experiments that demonstrate the
effectiveness of our proposed method for CTDA in the image domain,
outperforming the state-of-the-art results. Furthermore, our approach is also
evaluated in the 3D point cloud domain, showcasing its versatility and
potential for broader applicability.
