Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data. While achieving optimal accuracy, existing CTTA approaches present poor real-world applicability on resource-constrained edge devices, due to the substantial memory overhead and energy consumption. In this work, we first introduce a novel paradigm -- on-demand TTA -- which triggers adaptation only when a significant domain shift is detected. Then, we present OD-TTA, an on-demand TTA framework for accurate and efficient adaptation on edge devices. OD-TTA comprises three innovative techniques: 1) a lightweight domain shift detection mechanism to activate TTA only when it is needed, drastically reducing the overall computation overhead, 2) a source domain selection module that chooses an appropriate source model for adaptation, ensuring high and robust accuracy, 3) a decoupled Batch Normalization (BN) update scheme to enable memory-efficient adaptation with small batch sizes. Extensive experiments show that OD-TTA achieves comparable and even better performance while reducing the energy and computation overhead remarkably, making TTA a practical reality.

翻译：持续测试时自适应（CTTA）方法对每个输入批次数据持续调整部署模型。尽管现有CTTA方法能实现最优精度，但由于显著的内存开销与能耗，其在资源受限的边缘设备上缺乏实际可用性。本文首先提出一种新范式——按需测试时自适应（on-demand TTA），仅当检测到显著域偏移时触发自适应。继而提出OD-TTA框架，该框架在边缘设备上实现精准高效的自适应，包含三项创新技术：1）轻量级域偏移检测机制，仅在必要时激活TTA，大幅降低总体计算开销；2）源域选择模块，选择适配的源模型进行自适应，确保高鲁棒精度；3）解耦批归一化（BN）更新方案，支持小批量尺寸下的内存高效自适应。大量实验表明，OD-TTA在显著降低能耗与计算开销的同时，性能可媲美甚至超越现有方法，使TTA成为切实可行的技术方案。