论文标题
学习域纹理不变表示语义细分的域适应
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
论文作者
论文摘要
由于用于语义分割的注释像素级标签很费力，因此利用合成数据是一个有吸引力的解决方案。但是，由于合成域和实际域之间的域间隙，对于经过合成数据训练的模型以推广到真实数据是一项挑战。在本文中，将两个域之间的基本差异视为纹理，我们提出了一种适应目标域质地的方法。首先，我们使用样式转移算法多样性合成图像的纹理。生成的图像的各种纹理阻止分割模型过度拟合到一种特定（合成）纹理。然后，我们通过自我训练微调模型，以直接监督目标纹理。我们的结果实现了最新的性能，我们通过广泛的实验分析了在风格化数据集上训练的模型的属性。
Since annotating pixel-level labels for semantic segmentation is laborious, leveraging synthetic data is an attractive solution. However, due to the domain gap between synthetic domain and real domain, it is challenging for a model trained with synthetic data to generalize to real data. In this paper, considering the fundamental difference between the two domains as the texture, we propose a method to adapt to the texture of the target domain. First, we diversity the texture of synthetic images using a style transfer algorithm. The various textures of generated images prevent a segmentation model from overfitting to one specific (synthetic) texture. Then, we fine-tune the model with self-training to get direct supervision of the target texture. Our results achieve state-of-the-art performance and we analyze the properties of the model trained on the stylized dataset with extensive experiments.
