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：在线领域自适应在语义分割中的目标是处理部署期间不可预见的领域变化，例如突发天气事件。然而，与暴力自适应相关的高计算成本使得这一范式在现实应用中不可行。本文提出了HAMLET，一种面向硬件感知的模块化最低成本训练框架，用于实时领域自适应。我们的方法包括一个硬件感知的反向传播编排代理（HAMT）和一个专用的领域偏移检测器，能够主动控制模型何时以及如何适应（LT）。得益于这些进步，我们的方法能够在单个消费级GPU上以超过29FPS的速度执行语义分割并同时进行自适应。我们的框架在准确性和速度之间的良好权衡通过OnDA和SHIFT基准测试的实验结果得到验证。