Unsupervised domain adaptive object detection is a challenging vision task where object detectors are adapted from a label-rich source domain to an unlabeled target domain. Recent advances prove the efficacy of the adversarial based domain alignment where the adversarial training between the feature extractor and domain discriminator results in domain-invariance in the feature space. However, due to the domain shift, domain discrimination, especially on low-level features, is an easy task. This results in an imbalance of the adversarial training between the domain discriminator and the feature extractor. In this work, we achieve a better domain alignment by introducing an auxiliary regularization task to improve the training balance. Specifically, we propose Adversarial Image Reconstruction (AIR) as the regularizer to facilitate the adversarial training of the feature extractor. We further design a multi-level feature alignment module to enhance the adaptation performance. Our evaluations across several datasets of challenging domain shifts demonstrate that the proposed method outperforms all previous methods, of both one- and two-stage, in most settings.

翻译：无监督域自适应目标检测是一项具有挑战性的视觉任务，其目标是将目标检测器从标签丰富的源领域迁移至无标签的目标领域。近年来的进展证明了基于对抗的域对齐方法的有效性，即特征提取器与域判别器之间的对抗训练能够在特征空间中实现域不变性。然而，由于域偏移的存在，域判别（尤其是低层特征层面的判别）较为容易，这导致域判别器与特征提取器之间的对抗训练出现不平衡。本研究通过引入辅助正则化任务来改善训练平衡，从而实现更优的域对齐。具体而言，我们提出将对抗性图像重建（AIR）作为正则化器，以促进特征提取器的对抗训练。此外，我们还设计了一个多层特征对齐模块来增强自适应性能。在多个存在显著域偏移挑战的基准数据集上的评估表明，所提出的方法在大多数设置下均优于以往的所有单阶段与两阶段方法。