Semi-supervised Object Detection: A Survey on Recent Research and
  Progress
Yanyang Wang Zhaoxiang Liu Shiguo Lian
In recent years, deep learning technology has been maturely applied in the
field of object detection, and most algorithms tend to be supervised learning.
However, a large amount of labeled data requires high costs of human resources,
which brings about low efficiency and limitations. Semi-supervised object
detection (SSOD) has been paid more and more attentions due to its high
research value and practicability. It is designed to learn information by using
small amounts of labeled data and large amounts of unlabeled data. In this
paper, we present a comprehensive and up-to-date survey on the SSOD approaches
from five aspects. We first briefly introduce several ways of data
augmentation. Then, we dive the mainstream semi-supervised strategies into
pseudo labels, consistent regularization, graph based and transfer learning
based methods, and introduce some methods in challenging settings. We further
present widely-used loss functions, and then we outline the common benchmark
datasets and compare the accuracy among different representative approaches.
Finally, we conclude this paper and present some promising research directions
for the future. Our survey aims to provide researchers and practitioners new to
the field as well as more advanced readers with a solid understanding of the
main approaches developed over the past few years.