Object detection is a fundamental task in computer vision and image processing. Current deep learning based object detectors have been highly successful with abundant labeled data. But in real life, it is not guaranteed that each object category has enough labeled samples for training. These large object detectors are easy to overfit when the training data is limited. Therefore, it is necessary to introduce few-shot learning and zero-shot learning into object detection, which can be named low-shot object detection together. Low-Shot Object Detection (LSOD) aims to detect objects from a few or even zero labeled data, which can be categorized into few-shot object detection (FSOD) and zero-shot object detection (ZSD), respectively. This paper conducts a comprehensive survey for deep learning based FSOD and ZSD. First, this survey classifies methods for FSOD and ZSD into different categories and discusses the pros and cons of them. Second, this survey reviews dataset settings and evaluation metrics for FSOD and ZSD, then analyzes the performance of different methods on these benchmarks. Finally, this survey discusses future challenges and promising directions for FSOD and ZSD.

翻译：计算机视觉和图像处理是一项基本任务。 目前,基于深层学习的物体探测器在贴有标签的数据方面非常成功。 但是,在现实生活中,不能保证每个物体类别有足够的标签样本来进行培训。这些大型物体探测器在培训数据有限时很容易被过度使用。因此,有必要在物体探测中引入微小的学习和零光学习,这种探测可以同时命名为低发物体探测。低温物体探测(LSOD)旨在从少数甚至零的标签数据中探测物体,这些数据可以分别归类为几发物体探测(FSOD)和零发物体探测(ZSD),本文对基于FSOD和ZSD的深度学习进行了全面调查。首先,这项调查将FSOD和ZSD的方法分为不同类别,并讨论其利弊。第二,这项调查审查FSOD和ZSD的数据集设置和评估指标,然后分析这些基准的不同方法的性能。最后,这项调查讨论了FSOD和ZSD的未来挑战和前景。