ORE is a CVPR 2021 Oral paper introducing the Open World Object Detection problem where a model must identify unknown objects without explicit supervision and incrementally learn new classes without forgetting. The solution uses contrastive clustering to separate known and unknown instances and energy-based scores for unknown identification. It is built on Detectron2 and achieves state-of-the-art results in incremental object detection settings.