Continual Test-Time Adaptation (CTTA) aims to adapt models to sequentially changing domains during testing, relying on pseudo-labels for self-adaptation. However, incorrect pseudo-labels can accumulate, leading to performance degradation. To address this, we propose a Conformal Uncertainty Indicator (CUI) for CTTA, leveraging Conformal Prediction (CP) to generate prediction sets that include the true label with a specified coverage probability. Since domain shifts can lower the coverage than expected, making CP unreliable, we dynamically compensate for the coverage by measuring both domain and data differences. Reliable pseudo-labels from CP are then selectively utilized to enhance adaptation. Experiments confirm that CUI effectively estimates uncertainty and improves adaptation performance across various existing CTTA methods.
@article{arxiv.2502.02998,
title = {Conformal Uncertainty Indicator for Continual Test-Time Adaptation},
author = {Fan Lyu and Hanyu Zhao and Ziqi Shi and Ye Liu and Fuyuan Hu and Zhang Zhang and Liang Wang},
journal= {arXiv preprint arXiv:2502.02998},
year = {2025}
}