论文标题
FIFO：学习雾化的雾化特征，用于雾化场景细分
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation
论文作者
论文摘要
在不利的天气条件下，在现实世界中，强大的视觉识别非常重要。在这种情况下，我们提出了一种学习语义分割模型的新方法，可抵抗雾。它的关键思想是将图像的雾条件视为其样式，并在分割模型的神经风格空间中缩小具有不同雾化条件的图像之间的差距。特别是，由于图像的神经风格通常受其他因素和雾影响，因此我们引入了一个雾气通滤波器模块，该模块学会从样式中提取雾化因子。优化雾气过滤器和分割模型交替逐步缩小不同雾条件之间的样式差距，并允许学习雾化不变特征。我们的方法在三个真正的有雾图像数据集上大大优于先前的工作。此外，它可以提高雾气和清晰的天气图像的性能，而现有方法通常会在清晰的场景上降低性能。
Robust visual recognition under adverse weather conditions is of great importance in real-world applications. In this context, we propose a new method for learning semantic segmentation models robust against fog. Its key idea is to consider the fog condition of an image as its style and close the gap between images with different fog conditions in neural style spaces of a segmentation model. In particular, since the neural style of an image is in general affected by other factors as well as fog, we introduce a fog-pass filter module that learns to extract a fog-relevant factor from the style. Optimizing the fog-pass filter and the segmentation model alternately gradually closes the style gap between different fog conditions and allows to learn fog-invariant features in consequence. Our method substantially outperforms previous work on three real foggy image datasets. Moreover, it improves performance on both foggy and clear weather images, while existing methods often degrade performance on clear scenes.
