Scale-Aware Domain Adaptive Faster R-CNN

Object detection typically assumes that training and test samples are drawn from an identical distribution, which, however, does not always hold in practice. Such a distribution mismatch may lead to a significant performance drop. In this work, we present Scale-aware Domain Adaptive Faster R-CNN, a model aiming at improving the cross-domain robustness of object detection. In particular, our model improves the traditional Faster R-CNN model by tackling the domain shift on two levels: (1) the image-level shift, such as image style, illumination, etc., and (2) the instance-level shift, such as object appearance, size, etc. The two domain adaptation modules are implemented by learning domain classifiers in an adversarial training manner. Moreover, we observe that the large variance in object scales often brings a crucial challenge to cross-domain object detection. Thus, we improve our model by explicitly incorporating the object scale into adversarial training. We evaluate our proposed model on multiple cross-domain scenarios, including object detection in adverse weather, learning from synthetic data, and cross-camera adaptation, where the proposed model outperforms baselines and competing methods by a significant margin. The promising results demonstrate the effectiveness of our proposed model for cross-domain object detection. The implementation of our model is available at https://github.com/yuhuayc/sa-da-faster .