  Continual Learning (CL) focuses on developing algorithms with the ability to
adapt to new environments and learn new skills. This very challenging task has
generated a lot of interest in recent years, with new solutions appearing
rapidly. In this paper, we propose a nVFNet-RDC approach for continual object
detection. Our nVFNet-RDC consists of teacher-student models, and adopts replay
and feature distillation strategies. As the 1st place solutions, we achieve
55.94% and 54.65% average mAP on the 3rd CLVision Challenge Track 2 and Track
3, respectively.
