  Domain adaptive object detection (DAOD) aims to adapt the detector from a
labelled source domain to an unlabelled target domain. In recent years, DAOD
has attracted massive attention since it can alleviate performance degradation
due to the large shift of data distributions in the wild. To align
distributions between domains, adversarial learning is widely used in existing
DAOD methods. However, the decision boundary for the adversarial domain
discriminator may be inaccurate, causing the model biased towards the source
domain. To alleviate this bias, we propose a novel Frequency-based Image
Translation (FIT) framework for DAOD. First, by keeping domain-invariant
frequency components and swapping domain-specific ones, we conduct image
translation to reduce domain shift at the input level. Second, hierarchical
adversarial feature learning is utilized to further mitigate the domain gap at
the feature level. Finally, we design a joint loss to train the entire network
in an end-to-end manner without extra training to obtain translated images.
Extensive experiments on three challenging DAOD benchmarks demonstrate the
effectiveness of our method.
