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Target-Aware Auto-Augmentation for Unsupervised Domain Adaptive Object Detection

Recent researches show that data auto-augmentation strategies can enhance the performance of object detection models. However, the existing works mainly focus on in-domain generalization. There is still a blank in out-of-domain generalization. In this paper, for the first time, we propose an auto-augmentation problem under unsupervised domain adaptation scenarios. To solve this problem, we propose a simple yet effective target-aware auto-augmentation technique to search for an optimal data augmentation strategy on labeled source data, so as to boost the detection ability on the given unlabeled target data. Our method can be easily plugged into the existing domain adaptation methods. Extensive experiments have been carried out to verify the effectiveness.