arXiv:2505.00986v3 Announce Type: replace Abstract: 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 e
The proliferation of AI models on edge devices and in embodied systems highlights the immediate need for more resource-efficient adaptation techniques.
This development addresses a critical bottleneck for deploying advanced AI in real-world, resource-constrained environments, expanding the applicability of AI significantly.
AI models can now adapt to new data much more efficiently on edge devices, overcoming previous limitations of memory and energy consumption.
- · Edge AI developers
- · Robotics companies
- · IoT device manufacturers
- · Autonomous systems
- · Models requiring constant cloud connectivity for adaptation
- · Less efficient TTA frameworks
More sophisticated AI capabilities become viable on devices with limited computational resources.
This efficiency could accelerate the development and deployment of autonomous robots and smart devices in diverse environments.
Reduced energy consumption for AI operations could contribute to broader sustainability goals within the technology sector.
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