AN END-TO-END ARCHITECTURE FOR CLASS-INCREMENTAL OBJECT DETECTION WITH KNOWLEDGE DISTILLATION

Recent efforts have been made on continuously learning concepts over time, i.e., class-incremental learning, which, however, is still an open question to the task of object detection. For the first time, this paper systematically studies the task of learning an end-to-end class-incremental object detection model. Remarkably, this work tailors Faster R-CNN and proposes a Class-Incremental Faster R-CNN (CIFRCN) model that can dynamically add new classes by only using a few labeled images of new objects. Particularly, the domain of "foreground" in a Region Proposal Network is expanded to generate accurate bounding box proposals, and the classifier in Fast R-CNN is enlarged using knowledge distillation to accurately classify the proposals. Extensive experiments on two challenging datasets are conducted to demonstrate the efficacy of the proposed model for adding new objects in a class-incremental way, whereas other detectors quickly fail.