Training on synthetic data is becoming popular in vision due to the convenient acquisition of accurate pixel-level labels. But the domain gap between synthetic and real images significantly degrades the performance of the trained model. We propose a color space adaptation method to bridge the gap. A set of closed-form operations are adopted to make color space adjustments while preserving the labels. We embed these operations into a two-stage learning approach, and demonstrate the adaptation efficacy on the semantic segmentation task of cirrus clouds.

中文翻译：

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学习从卷云合成图像到真实图像的色彩空间适应

由于可以方便地获取准确的像素级标签，因此合成数据的训练在视觉领域变得越来越流行。但是合成图像和真实图像之间的域差距会显着降低训练模型的性能。我们提出了一种色彩空间适应方法来弥合差距。采用一组封闭形式的操作在保留标签的同时进行色彩空间调整。我们将这些操作嵌入到两阶段学习方法中，并展示了对卷云语义分割任务的适应效果。