Frequency Spectrum Augmentation Consistency for Domain Adaptive Object Detection

Abstract

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. And our code is avaliable at https://github.com/ruiliu-code/FSAC.

Diverse Feature-Level Guidance Adjustments for Unsupervised Domain Adaptative Object Detection

“…Regarding data distribution differences, Yang et al [30] and Liu et al [31] incorporated the exchange of features' low-frequency spectral information in the frequency domain between different domains. Furthermore, Hsu et al [32] proposed the intermediate domain, which generated synthetic data by Cycle GAN [33] to mimic target domain data.…”