Few-shot object detection, learning to adapt to the novel classes with a few
labeled data, is an imperative and long-lasting problem due to the inherent
long-tail distribution of real-world data and the urgent demands to cut costs
of data collection and annotation. Recently, some studies have explored how to
use implicit cues in extra datasets without target-domain supervision to help
few-shot detectors refine robust task notions. This survey provides a
comprehensive overview from current classic and latest achievements for
few-shot object detection to future research expectations from manifold
perspectives. In particular, we first propose a data-based taxonomy of the
training data and the form of corresponding supervision which are accessed
during the training stage. Following this taxonomy, we present a significant
review of the formal definition, main challenges, benchmark datasets,
evaluation metrics, and learning strategies. In addition, we present a detailed
investigation of how to interplay the object detection methods to develop this
issue systematically. Finally, we conclude with the current status of few-shot
object detection, along with potential research directions for this field.

本研究综述了少样本物体检测的现有成果及未来展望，提出了基于数据的训练分类法和相应监督的形式，并对其定义、主要挑战、基准数据集、评估指标和学习策略进行了详细调查，最后总结了少样本目标检测的现状以及未来的研究方向。