Continual test-time adaptation (CTTA) is the task of deployed models adapting to the massive data stream with potential distribution shifts in the test environment to maintain generalizability over time. Current research has primarily focused on two major challenges, catastrophic forgetting and error accumulation, while paying relatively little attention to adaptation efficiency. Moreover, although distribution shifts occur in a wide range of forms, most existing methods often ignore the differences among these shift types and rely on unified optimization strategies that remain insensitive to the magnitude of the shift, making them prone to overfitting for mild shifts and insufficiently adaptive under severe shifts. To address the above problems, a Shift-Level Regulated Continual Test-Time Adaptation Framework (SLRF) is proposed to determine the shift level and subsequently select the most appropriate adaptation strategy from a set of well-designed options. Therefore, SLRF mainly consists of two modules: an adaptive shift-level detector (ASLD) and an adaptation strategies decision module (ASDM). Considering the influence of distribution shifts on both generalization and adaptation, ASLD categorizes shift level into three types: aligned, weak shift, and strong shift. Based on the reliable ASLD results, ASDM selects the adaptation strategy that is constructed according to the detection criteria for efficient adaptation. Specifically, aligned samples are leveraged to maintain long-term adaptability; weak-shift samples are used to further improve generalization; strong-shift samples are utilized for domain alignment to avoid error accumulation and enable rapid adaptation. Furthermore, SLRF provides a stable mechanism for transitioning across different shift scenarios and preventing catastrophic forgetting, while maintaining low computation overhead. Extensive experiments on CIFAR10C, CIFAR100C, and ImageNet-C demonstrate that SLRF achieves superior performance in both generalization and adaptability.
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