The goal of Online Domain Adaptation for semantic segmentation is to handle
unforeseeable domain changes that occur during deployment, like sudden weather
events. However, the high computational costs associated with brute-force
adaptation make this paradigm unfeasible for real-world applications. In this
paper we propose HAMLET, a Hardware-Aware Modular Least Expensive Training
framework for real-time domain adaptation. Our approach includes a
hardware-aware back-propagation orchestration agent (HAMT) and a dedicated
domain-shift detector that enables active control over when and how the model
is adapted (LT). Thanks to these advancements, our approach is capable of
performing semantic segmentation while simultaneously adapting at more than
29FPS on a single consumer-grade GPU. Our framework's encouraging accuracy and
speed trade-off is demonstrated on OnDA and SHIFT benchmarks through
experimental results.

HAMLET 是为实时领域自适应设计的硬件感知模块化最低成本训练框架，通过硬件感知反向传播编排代理（HAMT）和专用领域转移检测器实现主动控制模型适应的时间和方式，在单个消费级 GPU 上以超过 29FPS 的速度执行语义分割的同时进行自适应。实验结果在 OnDA 和 SHIFT 基准上证明了我们框架的令人鼓舞的准确性和速度折衷。