When the training data and the test data do not obey the same distribution, the performances of many object detection methods always decrease greatly. Naturally, domain adaptation methods at feature level are proposed. The basic idea is to adapt the feature extraction network such that the feature distributions of the source and target domains match. We propose a new method that is built directly on the Faster R-CNN model, which not only aligns the source and target data features, but also forces their generated features closer together to further align the source and target domains. Moreover, compared with previous approaches, we construct a more powerful discriminator and a simple generator to solve the domain adaptation problem. The model works like a pair of scissors, so we call it Scissors Networks (SN). We conduct extensive experiments on popular datasets, including Cityscapes, Foggy Cityscapes, SIM10k and KITTI. The experimental results demonstrate that our algorithm is superior to the state-of-the-art deep learning based domain adaptation approaches.

当训练数据和测试数据不服从相同分布时，许多目标检测方法的性能总是大大降低。自然地，提出了特征级别的域自适应方法。基本思想是调整特征提取网络，以使源域和目标域的特征分布匹配。我们提出了一种直接建立在Faster R-CNN模型上的新方法，该方法不仅可以使源数据特征和目标数据特征对齐，而且可以将它们生成的特征更紧密地结合在一起以进一步使源域和目标域对齐。而且，与以前的方法相比，我们构造了一个功能更强大的鉴别器和一个简单的生成器来解决域自适应问题。该模型就像一把剪刀一样工作，因此我们将其称为“剪刀网络”（SN）。我们对流行的数据集进行了广泛的实验，包括Cityscapes，Foggy Cityscapes，SIM10k和KITTI。实验结果表明，我们的算法优于基于深度学习的最新领域自适应方法。