Object detection based on deep learning has been enormously developed in recent years. However, applying the detectors trained on a label-rich domain to an unseen domain results in performance drop due to the domain-shift. To deal with this problem, we propose a novel unsupervised domain adaptation method to adapt from a labeled source domain to an unlabeled target domain. Recent approaches based on adversarial learning show some effect for aligning the feature distributions of different domains, but the decision boundary would be strongly source-biased for the complex detection task when merely training with source labels and aligning in the entire feature distribution. In this paper, we suggest utilizing image translation to generate translated images of source and target domains to fill in the large domain gap and facilitate a paired adaptation. We propose a hierarchical contrastive adaptation method between the original and translated domains to encourage the detectors to learn domain-invariant but discriminative features. To attach importance to foreground instances and tackle the noises of translated images, we further propose foreground attention reweighting for instance-aware adaptation . Experiments are carried out on 3 cross-domain detection scenarios, and we achieve the state-of-the-art results against other approaches, showing the effectiveness of our proposed method.

近年来，基于深度学习的目标检测得到了极大的发展。但是，将在标签丰富的域上训练的检测器应用于看不见的域会由于域转移而导致性能下降。为了解决这个问题，我们提出了一种新的无监督域适应方法来适应从标记的源域到未标记的目标域。最近基于对抗性学习的方法在对齐不同域的特征分布方面显示出一些效果，但是当仅使用源标签进行训练并在整个特征分布中对齐时，对于复杂的检测任务，决策边界将具有很强的源偏差。在本文中，我们建议利用图像翻译来生成源域和目标域的翻译图像，以填补巨大的域空白并促进配对适应。我们提出了一种原始域和翻译域之间的分层对比适应方法，以鼓励检测器学习域不变但有区别的特征。为了重视前景实例并解决翻译图像的噪声，我们进一步提出前景注意力重新加权以进行实例感知适应。在 3 个跨域检测场景上进行了实验，我们实现了与其他方法相比的最新结果，显示了我们提出的方法的有效性。我们进一步提出了用于实例感知适应的前景注意力重新加权。在 3 个跨域检测场景上进行了实验，我们实现了与其他方法相比的最新结果，显示了我们提出的方法的有效性。我们进一步提出了用于实例感知适应的前景注意力重新加权。在 3 个跨域检测场景上进行了实验，我们实现了与其他方法相比的最新结果，显示了我们提出的方法的有效性。