Continual Test-Time Adaptation (CTTA) is essential for adapting models to target data in changing environments while retaining prior knowledge. However, previous methods overlook class imbalance, which limits performance on minor but essential objects. To address this, we propose AdaCoTTA, which applies confidence-guided adaptive learning to improve training stability. These mechanisms mitigate class imbalance, catastrophic forgetting, and error accumulation in CTTA. We evaluate AdaCoTTA on diverse CTTA benchmarks for semantic segmentation. It achieves state-of-the-art performance, improving average mIoU by 0.6% on ACDC and over 4% for minor classes, highlighting its effectiveness in scenarios. Code is available at https://github.com/junghyeon0427/AdaCoTTA .
Seo et al. (Wed,) studied this question.