Continual Test-Time Adaptation (TTA) seeks to adapt a source pre-trained
model to continually changing, unlabeled target domains. Existing TTA methods
are typically designed for environments where domain changes occur gradually
and can struggle in more dynamic scenarios. Inspired by the principles of
online K-Means, this paper introduces a novel approach to continual TTA through
visual prompting. We propose a Dynamic Prompt Coreset that not only preserves
knowledge from previously visited domains but also accommodates learning from
new potential domains. This is complemented by a distance-based weight updating
mechanism that ensures the coreset remains current and relevant. Our approach
employs a fixed model architecture alongside the coreset and an innovative
updating system to effectively mitigate challenges such as catastrophic
forgetting and error accumulation. Extensive testing across various
benchmarks-including ImageNet-C, CIFAR100-C, and CIFAR10-C-demonstrates that
our method consistently outperforms state-of-the-art (SOTA) alternatives,
particularly excelling in dynamically changing environments.

本文介绍了一种通过视觉提示的动态提示核心集方法来实现持续的测试时间自适应（TTA），该方法不仅保留了先前访问的领域的知识，而且适应了新潜在领域的学习。经过广泛的测试，包括 ImageNet-C，CIFAR100-C 和 CIFAR10-C 等基准，显示出我们的方法始终优于最先进的替代方法，在动态变化的环境中表现突出，特别是在遇到灾难性遗忘和误差累积等挑战时。