Semantic segmentation is a structured prediction problem that heavily relies on expensive annotated image data to train supervised models. Unsupervised domain adaptation has been successful in leveraging synthetic (source) images to build models that generalize well to real (target) image data without annotations. However, previous methods mainly utilize source ground truth for segmentation loss and do not fully utilize them for learning segmentation output structures to guide the target domain. In this work, we exploit similar output structures across domains in order to better segment the target images. Toward this end, we devise an adversarial structured prediction by utilizing a regularizer. This regularizer outputs structured predictions on provided image features. Using an adversarial training setup, we make the structured predictions follow the spatial layout learned from the source ground truth. As a result, even without an explicit alignment between source and target features, our proposed method can adapt well from a source to a target domain. We evaluate our method on different challenging synthetic-2-real benchmarks and validate the effectiveness of the proposed method when compared with the state of the arts.

语义分割是一个结构化的预测问题，它严重依赖昂贵的注释图像数据来训练监督模型。无监督域适应已经成功地利用合成（源）图像来构建模型，该模型可以很好地泛化到没有注释的真实（目标）图像数据。然而，以前的方法主要利用源地面实况进行分割损失，并没有充分利用它们来学习分割输出结构来指导目标域。在这项工作中，我们利用跨域的相似输出结构来更好地分割目标图像。为此，我们利用正则化器设计了一种对抗性结构化预测。这个正则化器输出对提供的图像特征的结构化预测。使用对抗性训练设置，我们使结构化预测遵循从源地面实况中学习的空间布局。因此，即使源特征和目标特征之间没有明确的对齐，我们提出的方法也可以很好地从源域适应目标域。我们在不同的具有挑战性的 synthetic-2-real 基准上评估我们的方法，并与现有技术相比验证所提出方法的有效性。