论文标题
SS-SFDA：在危险环境中，自我监督的无源领域适应道路分割
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous Environments
论文作者
论文摘要
我们提出了一种在不利天气条件（例如雨水或雾气）中无监督的道路细分的新方法。这包括一种使用自我监督学习的新算法，用于无源域适应（SFDA）。此外，我们的方法使用多种技术来应对SFDA的各种挑战并提高性能，包括在线生成伪标签和自我注意力以及使用课程学习，熵最小化和模型蒸馏。我们已经评估了与真实和合成不利天气条件相对应的$ 6 $数据集的性能。我们的方法的表现优于无监督的道路细分和SFDA的所有先前工作，至少提高了10.26％，并将培训时间提高了18-180x。此外，与先前的监督方法相比，我们的自我监管算法在MIOU评分方面表现出相似的精度性能。
We present a novel approach for unsupervised road segmentation in adverse weather conditions such as rain or fog. This includes a new algorithm for source-free domain adaptation (SFDA) using self-supervised learning. Moreover, our approach uses several techniques to address various challenges in SFDA and improve performance, including online generation of pseudo-labels and self-attention as well as use of curriculum learning, entropy minimization and model distillation. We have evaluated the performance on $6$ datasets corresponding to real and synthetic adverse weather conditions. Our method outperforms all prior works on unsupervised road segmentation and SFDA by at least 10.26%, and improves the training time by 18-180x. Moreover, our self-supervised algorithm exhibits similar accuracy performance in terms of mIOU score as compared to prior supervised methods.
