Foreground-Background Imbalance Problem in Deep Object Detectors: A Review
This is an incremental review paper that addresses a known bottleneck in training accurate deep object detectors for computer vision applications.
The paper reviews the foreground-background imbalance problem in deep object detectors, analyzing its characteristics in different detector types and categorizing solutions into sampling heuristics and non-sampling schemes, with experimental comparisons on the COCO benchmark.
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.