Class-Wise Buffer Management for Incremental Object Detection: An
  Effective Buffer Training Strategy
Junsu Kim Sumin Hong Chanwoo Kim Jihyeon Kim Yihalem Yimolal Tiruneh Jeongwan On Jihyun Song Sunhwa Choi Seungryul Baek
Class incremental learning aims to solve a problem that arises when
continuously adding unseen class instances to an existing model This approach
has been extensively studied in the context of image classification; however
its applicability to object detection is not well established yet. Existing
frameworks using replay methods mainly collect replay data without considering
the model being trained and tend to rely on randomness or the number of labels
of each sample. Also, despite the effectiveness of the replay, it was not yet
optimized for the object detection task. In this paper, we introduce an
effective buffer training strategy (eBTS) that creates the optimized replay
buffer on object detection. Our approach incorporates guarantee minimum and
hierarchical 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 optimally
utilize the accumulated buffer data. Experiments on the MS COCO dataset
demonstrate that our eBTS achieves state-of-the-art performance compared to the
existing replay schemes.