Recently, great achievements have been made for deep learning based object detection methods. But their performance drops significantly when domain shifts occur. To address this problem, in this work we propose a loose to compact feature alignment method under an unsupervised domain adaptation framework. The entire feature alignment is performed in a manner, so as to distribute the alignment difficulties at two steps. At the first step, we loosen the goal at both image and instance levels. At the image level, a new Mask Guided Foreground Alignment (MGFA) module is proposed to make the alignment focus more on easier foreground regions, leaving the more diverse and more difficult background regions to the second step; at the instance level, we propose a Class-Wise Instance Alignment (CWIA) module with separated domain classifiers for different categories so as to ease the alignment. At the second step, the alignment is performed per pixel and per instance, achieving a more compact and better aligned feature space. We conduct experiments on three different adaptation scenarios, where we achieve comparable results, demonstrating the effectiveness of our proposed method.

中文翻译：

---

用于域自适应对象检测的松散到紧凑的特征对齐

近年来，基于深度学习的目标检测方法取得了巨大的成就。但当发生域转移时，它们的性能会显着下降。为了解决这个问题，在这项工作中，我们提出了一种在无监督域适应框架下从松散到紧凑的特征对齐方法。整个特征对齐以将对齐困难分散在两个步骤的方式进行。第一步，我们放松了图像和实例级别的目标。在图像层面，提出了一种新的掩模引导前景对齐（MGFA）模块，使对齐更多地集中在更容易的前景区域，将更多样化和更困难的背景区域留给第二步；在实例级别，我们提出了一个类明智实例对齐（CWIA）模块，其中针对不同类别使用单独的域分类器，以简化对齐。在第二步中，对每个像素和每个实例执行对齐，实现更紧凑和更好对齐的特征空间。我们对三种不同的适应场景进行了实验，获得了可比较的结果，证明了我们提出的方法的有效性。