Research on Fourier Augmented Unbiased Cross-Domain Object Detection

Abstract

The main purpose of unbiased cross-domain object detection is to utilize the knowledge of the source domain to the maximum extent through knowledge distillation, and reduce the cross-domain gap of the model through domain adaptation. However, the pseudo labels generated by the mean teacher method commonly used for unbiased cross-domain object detection are not reliable, resulting in significant domain bias issues between teacher and student models. Therefore, inspired by the invariance of phase information in Fourier transform, this paper proposes the Fourier augmentation unbiased mean teacher (FAUMT) model based on the mean teacher. This paper utilizes the invariance of Fourier phase information to design an amplitude mixing data augmentation (AMDA) module, which can effectively mix phase information between the source and target domains to achieve data augmentation. And data augmentation will generate additional noise, thus this paper designs two consistency losses to ensure the consistency of predictions before and after data augmentation. In addition, to balance the cross-domain bias between the source and target domains during model training, this paper also designs a multi-layer adversarial learning (MAL) module, with the aim of domain alignment of pixel level features at different levels. On three benchmark datasets Cilpart1K, Watercolor2K and Comic2K, the mAP of proposed method achieves 47.5%, 58.9% and 46.1%, respectively, outperforming other algorithms.

Keywords

• domain adaptation; cross-domain object detection; deep neural network; mean teacher