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Class incremental learning aims to solve a problem that arises whencontinuously adding unseen class instances to an existing model This approachhas been extensively studied in the context of image classification; howeverits applicability to object detection is not well established yet. Existingframeworks using replay methods mainly collect replay data without consideringthe model being trained and tend to rely on randomness or the number of labelsof each sample. Also, despite the effectiveness of the replay, it was not yetoptimized for the object detection task. In this paper, we introduce aneffective buffer training strategy (eBTS) that creates the optimized replaybuffer on object detection. Our approach incorporates guarantee minimum andhierarchical sampling to establish the buffer customized to the trained model.%These methods can facilitate effective retrieval of prior knowledge.Furthermore, we use the circular experience replay training to optimallyutilize the accumulated buffer data. Experiments on the MS COCO datasetdemonstrate that our eBTS achieves state-of-the-art performance compared to theexisting replay schemes.

Class-Wise Buffer Management for Incremental Object Detection: An Effective Buffer Training Strategy