适应还是不适应？语义分割的实时自适应

摘要

在线域自适应语义分割的目标是处理部署过程中发生的无法预见的域变化，如突发天气事件。然而，与蛮力自适应相关的高计算成本使得这种范式在实际应用中不可行。本文提出了HAMLET，一种用于实时域自适应的硬件感知模块化最经济训练框架。我们的方法包括硬件感知反向传播编排代理（HAMT）和一个专用的领域转移检测器，它使模型何时以及如何进行适应（LT）能够进行主动控制。由于这些进展，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同时进行适应。通过实验结果，我们的框架在OnDA和SHIFT基准测试中展示了令人鼓舞的准确性和速度权衡。

English

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.

适应还是不适应？语义分割的实时自适应

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

摘要

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