Region Proposal Oriented Approach for Domain Adaptive Object Detection

Abstract

Faster R-CNN has become a standard model in deep-learning based object detection. However, in many cases, few annotations are available for images in the application domain referred as the target domain whereas full annotations are available for closely related public or synthetic datasets referred as source domains. Thus, a domain adaptation is needed to be able to train a model performing well in the target domain with few or no annotations in this target domain. In this work, we address this domain adaptation problem in the context of object detection in the case where no annotations are available in the target domain. Most existing approaches consider adaptation at both global and instance level but without adapting the region proposal sub-network leading to a residual domain shift. After a detailed analysis of the classical Faster R-CNN detector, we show that adapting the region proposal sub-network is crucial and propose an original way to do it. We run experiments in two different application contexts, namely autonomous driving and ski-lift video surveillance, and show that our adaptation scheme clearly outperforms the previous solution.