Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Foreground-Background Imbalance Problem in Deep Object Detectors: A
  Review   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.

31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph