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.Comment: Accepted at IEEE Robotics and Automation Letters 202