  The continual learning problem has been widely studied in image
classification, while rare work has been explored in object detection. Some
recent works apply knowledge distillation to constrain the model to retain old
knowledge, but this rigid constraint is detrimental for learning new knowledge.
In our paper, we propose a new scheme for continual learning of object
detection, namely Contrast R-CNN, an approach strikes a balance between
retaining the old knowledge and learning the new knowledge. Furthermore, we
design a Proposal Contrast to eliminate the ambiguity between old and new
instance to make the continual learning more robust. Extensive evaluation on
the PASCAL VOC dataset demonstrates the effectiveness of our approach.
