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.

翻译：在线域自适应语义分割的目标是处理部署过程中不可预见的域变化，例如突发的天气事件。然而，暴力自适应方法的高计算成本使得该范式在实际应用中不可行。本文提出了HAMLET（硬件感知模块化最低成本训练框架），用于实时域自适应。我们的方法包括一个硬件感知的反向传播编排智能体（HAMT）和一个专用的域偏移检测器，能够主动控制模型何时以及如何自适应（LT）。得益于这些改进，我们的方法能够在单块消费级GPU上以超过29FPS的速度同时执行语义分割与自适应。通过在OnDA和SHIFT基准上的实验结果，我们框架的准确率与速度权衡表现令人鼓舞。