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。

实时语义分割的适应与否问题

To Adapt or Not to Adapt? Real-Time Adaptation for Semantic Segmentation

Unsupervised Domain Adaptation (UDA) aims at reducing the domain gap between
training and testing data and is, in most cases, carried out in offline manner.
However, domain changes may occur continuously and unpredictably during
deployment (e.g. sudden weather changes). In such conditions, deep neural
networks witness dramatic drops in accuracy and offline adaptation may not be
enough to contrast it. In this paper, we tackle Online Domain Adaptation (OnDA)
for semantic segmentation. We design a pipeline that is robust to continuous
domain shifts, either gradual or sudden, and we evaluate it in the case of
rainy and foggy scenarios. Our experiments show that our framework can
effectively adapt to new domains during deployment, while not being affected by
catastrophic forgetting of the previous domains.

本文介绍了一种针对语义分割的在线域自适应框架，能够有效地适应不断变化的域，包括降雨和雾霾天气，同时避免了之前域的灾难性遗忘现象。