  Recent years have witnessed the remarkable developments made by deep learning
techniques for object detection, a fundamentally challenging problem of
computer vision. Nevertheless, there are still difficulties in training
accurate deep object detectors, one of which is owing to the
foreground-background imbalance problem. In this paper, we survey the recent
advances about the solutions to the imbalance problem. First, we analyze the
characteristics of the imbalance problem in different kinds of deep detectors,
including one-stage and two-stage ones. Second, we divide the existing
solutions into two categories: sampling heuristics and non-sampling schemes,
and review them in detail. Third, we experimentally compare the performance of
some state-of-the-art solutions on the COCO benchmark. Promising directions for
future work are also discussed.
