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Humans can incrementally learn to do new visual detection tasks, which is ahuge challenge for today's computer vision systems. Incrementally trained deeplearning models lack backwards transfer to previously seen classes and sufferfrom a phenomenon known as $"catastrophic forgetting."$ In this paper, wepioneer online streaming learning for object detection, where an agent mustlearn examples one at a time with severe memory and computational constraints.In object detection, a system must output all bounding boxes for an image withthe correct label. Unlike earlier work, the system described in this paper canlearn this task in an online manner with new classes being introduced overtime. We achieve this capability by using a novel memory replay mechanism thatefficiently replays entire scenes. We achieve state-of-the-art results on boththe PASCAL VOC 2007 and MS COCO datasets.

RODEO: Replay for Online Object Detection