论文标题
语义上的语义自适应图像到图像翻译，用于域的语义分割
Semantically Adaptive Image-to-image Translation for Domain Adaptation of Semantic Segmentation
论文作者
论文摘要
域转移是语义细分的一个非常具有挑战性的问题。任何模型都可以在合成数据上很容易训练，其中图像和标签是人为生成的，但是在部署在真实环境中时的性能很差。在本文中，我们解决了针对街头场景语义分割的域适应性问题。许多最先进的方法着重于翻译源图像，同时强加了结果在语义上与输入一致。但是，我们主张的是，也可以利用图像语义来指导翻译算法。为此，我们重新考虑生成模型以实施这一假设并加强像素级和特征级域对齐之间的连接。我们通过使用我们的方法训练常见的语义分割模型进行广泛的实验，并表明我们在合成至现实基准测试的结果中获得的结果超过了最新的。
Domain shift is a very challenging problem for semantic segmentation. Any model can be easily trained on synthetic data, where images and labels are artificially generated, but it will perform poorly when deployed on real environments. In this paper, we address the problem of domain adaptation for semantic segmentation of street scenes. Many state-of-the-art approaches focus on translating the source image while imposing that the result should be semantically consistent with the input. However, we advocate that the image semantics can also be exploited to guide the translation algorithm. To this end, we rethink the generative model to enforce this assumption and strengthen the connection between pixel-level and feature-level domain alignment. We conduct extensive experiments by training common semantic segmentation models with our method and show that the results we obtain on the synthetic-to-real benchmarks surpass the state-of-the-art.
