DTS-Meta: Dual-Timescale Meta-Learning for Continual Test-Time Adaptation

Continual test-time adaptation (CTTA) adapts a source-pretrained model to an unlabeled, non-stationary target stream without source data or domain boundaries. Existing feature-subspace methods identify where to adapt, but coordinating persistent task-related adjustments with rapid environment-dependent corrections remains insufficiently explored. We observe that class-sensitive subspaces remain relatively stable across batches, whereas domain-sensitive subspaces exhibit greater temporal variation and environmental sensitivity. We therefore propose DTS-META, a dual-timescale feature adapter with slow class-sensitive and fast domain-sensitive pathways. The slow pathway learns persistent modulation along directions maintained through smoothed classifier-sensitivity statistics to preserve task-relevant feature structure. The fast pathway responds to the current environment via a more frequently refreshed prototype-residual covariance basis, with a MetaNet mapping projected batch statistics and teacher uncertainty to bounded scale and bias. We train the MetaNet through episodic meta-learning, encouraging updates learned from the current batch to remain effective across input variations. Across three classification benchmarks and continual semantic segmentation on CarlaTTA, DTS-META consistently outperforms existing baselines, demonstrating the benefits of timescale coordination in non-stationary adaptation.