Recent studies of driving scene understanding based on image semantic segmentation have achieved dramatic advances in speed and accuracy. Large-scale public datasets for semantic segmentation of paved road driving scenes have led the advances, but there is no large-scale public dataset for unpaved road environments. Building a large-scale image semantic segmentation dataset for unpaved roads is very expensive, and domain gaps between geographically distributed locations and those of seasonal changes hinder building a training dataset that is adequate to train a convolutional neural network model. In this paper, to resolve the data insufficiency problem, we use an one-shot learning setting in unpaved road driving scene understanding. Our One-shot Summary Prototypical Network (OSPNet) is trained with paved road driving scenes, and it identifies drivable regions in unpaved roads given only a single support image and unpaved road mask data. The OSPNet improves previous two branch few-shot segmentation approaches by introducing the summary branch which enables channel-wise weighting for important features in the feature map of support and query branches. Our experiments show that our model quantitatively and qualitatively outperforms recent supervised and few-shot segmentation models.

中文翻译：

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一次总结原型网络实现准确的未铺砌道路语义分割

最近基于图像语义分割的驾驶场景理解研究在速度和准确性方面取得了巨大进步。用于铺装道路驾驶场景语义分割的大规模公共数据集已经领先，但没有用于未铺装道路环境的大规模公共数据集。为未铺砌道路构建大规模图像语义分割数据集非常昂贵，地理分布位置和季节性变化之间的域差距阻碍了构建足以训练卷积神经网络模型的训练数据集。在本文中，为了解决数据不足问题，我们在未铺砌道路驾驶场景理解中使用一次性学习设置。我们的一次性摘要原型网络 (OSPNet) 经过铺砌道路驾驶场景训练，它在仅给定单个支持图像和未铺砌道路蒙版数据的情况下识别未铺砌道路中的可行驶区域。OSPNet 通过引入汇总分支改进了前两个分支的小样本分割方法，该汇总分支为支持和查询分支的特征图中的重要特征启用通道加权。我们的实验表明，我们的模型在数量和质量上都优于最近的监督和小样本分割模型。