  Domain adaptive object detection (DAOD) aims to improve the generalization
ability of detectors when the training and test data are from different
domains. Considering the significant domain gap, some typical methods, e.g.,
CycleGAN-based methods, adopt the intermediate domain to bridge the source and
target domains progressively. However, the CycleGAN-based intermediate domain
lacks the pix- or instance-level supervision for object detection, which leads
to semantic differences. To address this problem, in this paper, we introduce a
Frequency Spectrum Augmentation Consistency (FSAC) framework with four
different low-frequency filter operations. In this way, we can obtain a series
of augmented data as the intermediate domain. Concretely, we propose a
two-stage optimization framework. In the first stage, we utilize all the
original and augmented source data to train an object detector. In the second
stage, augmented source and target data with pseudo labels are adopted to
perform the self-training for prediction consistency. And a teacher model
optimized using Mean Teacher is used to further revise the pseudo labels. In
the experiment, we evaluate our method on the single- and compound- target DAOD
separately, which demonstrate the effectiveness of our method.

Computer Vision and Pattern Recognition;Artificial Intelligence