Cross-Domain Object Detection with Missing Classes in Target Domain

Many existing methods focus on detecting either objects from different domains or those of rare classes, but it's difficult for them to tackle the two issues together. However, in the real world, due to the difficulty of collecting samples of special classes, deep learning practitioners have to use simulated images to substitute for them. To deal with this scenario, in this paper, we research a new task: cross-domain object detection with missing classes in target domain, where there are only partial classes have images and annotations in the target domain. We devise a simple but effective play-and-plug method to address this new task, named the three-stage learning approach with domain and class information preservation. In addition, extensive experiments demonstrate our method is effective and can boost the performance when added to existing unsupervised domain adaptation object detectors.