Continual Test-Time Adaptation (CTTA) is proposed to migrate a source
pre-trained model to continually changing target distributions, addressing
real-world dynamism. Existing CTTA methods mainly rely on entropy minimization
or teacher-student pseudo-labeling schemes for knowledge extraction in
unlabeled target domains. However, dynamic data distributions cause
miscalibrated predictions and noisy pseudo-labels in existing self-supervised
learning methods, hindering the effective mitigation of error accumulation and
catastrophic forgetting problems during the continual adaptation process. To
tackle these issues, we propose a continual self-supervised method, Adaptive
Distribution Masked Autoencoders (ADMA), which enhances the extraction of
target domain knowledge while mitigating the accumulation of distribution
shifts. Specifically, we propose a Distribution-aware Masking (DaM) mechanism
to adaptively sample masked positions, followed by establishing consistency
constraints between the masked target samples and the original target samples.
Additionally, for masked tokens, we utilize an efficient decoder to reconstruct
a hand-crafted feature descriptor (e.g., Histograms of Oriented Gradients),
leveraging its invariant properties to boost task-relevant representations.
Through conducting extensive experiments on four widely recognized benchmarks,
our proposed method attains state-of-the-art performance in both classification
and segmentation CTTA tasks.

提出了持续测试时间适应（CTTA），用于将源预训练模型迁移到不断变化的目标分布，以应对现实世界的动态变化。通过构建适应性分布遮罩自编码器（ADMA）来提取目标领域知识并缓解分布转移累积的问题，实验结果表明在分类和分割 CTTA 任务中达到了最先进的性能。