Object detection has achieved a huge breakthrough with deep neural networks and massive annotated data. However, current detection methods cannot be directly transferred to the scenario where the annotated data is scarce due to the severe overfitting problem. Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task. Low-Shot Object Detection (LSOD) is an emerging research topic of detecting objects from a few or even no annotated samples, consisting of One-Shot Object Detection (OSOD), Few-Shot Object Detection (FSOD) and Zero-Shot Object Detection (ZSD). This survey provides a comprehensive review of LSOD methods. First, we propose a thorough taxonomy of LSOD methods and analyze them systematically, comprising some extensional topics of LSOD (semi-supervised LSOD, weakly-supervised LSOD, and incremental LSOD). Then, we indicate the pros and cons of current LSOD methods with a comparison of their performance. Finally, we discuss the challenges and promising directions of LSOD to provide guidance for future works.

翻译：目标检测凭借深度神经网络和海量标注数据取得了巨大突破。然而，由于严重的过拟合问题，当前检测方法无法直接迁移至标注数据稀缺的场景。尽管小样本学习与零样本学习在图像分类领域已得到广泛研究，但目标检测在数据稀缺场景下需要设计新方法，因其额外包含具有挑战性的定位任务。小样本目标检测（LSOD）是一个新兴研究方向，旨在从少量甚至无标注样本中检测目标，涵盖单样本目标检测（OSOD）、小样本目标检测（FSOD）和零样本目标检测（ZSD）。本综述对LSOD方法进行了全面回顾。首先，我们提出了一套完整的LSOD方法分类体系，并系统分析了这些方法，包括LSOD的扩展主题（半监督LSOD、弱监督LSOD和增量LSOD）。随后，我们通过性能对比指出了当前LSOD方法的优缺点。最后，我们讨论了LSOD面临的挑战和有前景的研究方向，为未来工作提供指导。