  For real-world applications, neural network models are commonly deployed in
dynamic environments, where the distribution of the target domain undergoes
temporal changes. Continual Test-Time Adaptation (CTTA) has recently emerged as
a promising technique to gradually adapt a source-trained model to test data
drawn from a continually changing target domain. Despite recent advancements in
addressing CTTA, two critical issues remain: 1) The use of a fixed threshold
for pseudo-labeling in existing methodologies leads to the generation of
low-quality pseudo-labels, as model confidence varies across categories and
domains; 2) While current solutions utilize stochastic parameter restoration to
mitigate catastrophic forgetting, their capacity to preserve critical
information is undermined by its intrinsic randomness. To tackle these
challenges, we present CTAOD, aiming to enhance the performance of detection
models in CTTA scenarios. Inspired by prior CTTA works for effective
adaptation, CTAOD is founded on the mean-teacher framework, characterized by
three core components. Firstly, the object-level contrastive learning module
tailored for object detection extracts object-level features using the
teacher's region of interest features and optimizes them through contrastive
learning. Secondly, the dynamic threshold strategy updates the
category-specific threshold based on predicted confidence scores to improve the
quality of pseudo-labels. Lastly, we design a data-driven stochastic
restoration mechanism to selectively reset inactive parameters using the
gradients as weights for a random mask matrix, thereby ensuring the retention
of essential knowledge. We demonstrate the effectiveness of our approach on
four CTTA tasks for object detection, where CTAOD outperforms existing methods,
especially achieving a 3.0 mAP improvement on the Cityscapes-to-Cityscapes-C
CTTA task.
