  Incremental object detection aims to simultaneously maintain old-class
accuracy and detect emerging new-class objects in incremental data. Most
existing distillation-based methods underperform when unlabeled old-class
objects are absent in the incremental dataset. While the absence can be
mitigated by generating old-class samples, it also incurs high computational
costs. In this paper, we argue that the extra computational cost stems from the
inconsistency between the detector and the generative model, along with
redundant generation. To overcome this problem, we propose Efficient Generated
Object Replay (EGOR). Specifically, we generate old-class samples by inversing
the original detectors, thus eliminating the necessity of training and storing
additional generative models. We also propose augmented replay to reuse the
objects in generated samples, thereby reducing the redundant generation. In
addition, we propose high-response knowledge distillation focusing on the
knowledge related to the old class, which transfers the knowledge in generated
objects to the incremental detector. With the addition of the generated objects
and losses, we observe a bias towards old classes in the detector. We balance
the losses for old and new classes to alleviate the bias, thereby increasing
the overall detection accuracy. Extensive experiments conducted on MS COCO 2017
demonstrate that our method can efficiently improve detection performance in
the absence of old-class objects.
