The generic object detection (GOD) task has been successfully tackled by recent deep neural networks, trained by an avalanche of annotated training samples from some common classes. However, it is still non-trivial to generalize these object detectors to the novel long-tailed object classes, which have only few labeled training samples. To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes. Especially, the research in this emerging field has been flourishing in recent years with various benchmarks, backbones, and methodologies proposed. To review these FSOD works, there are several insightful FSOD survey articles [58, 59, 74, 78] that systematically study and compare them as the groups of fine-tuning/transfer learning, and meta-learning methods. In contrast, we review the existing FSOD algorithms from a new perspective under a new taxonomy based on their contributions, i.e., data-oriented, model-oriented, and algorithm-oriented. Thus, a comprehensive survey with performance comparison is conducted on recent achievements of FSOD. Furthermore, we also analyze the technical challenges, the merits and demerits of these methods, and envision the future directions of FSOD. Specifically, we give an overview of FSOD, including the problem definition, common datasets, and evaluation protocols. The taxonomy is then proposed that groups FSOD methods into three types. Following this taxonomy, we provide a systematic review of the advances in FSOD. Finally, further discussions on performance, challenges, and future directions are presented.

翻译：通用目标检测（GOD）任务已通过最新深度神经网络成功解决，这些网络基于常见类别的海量标注训练样本进行训练。然而，将这些检测器推广至仅有少量标注训练样本的新型长尾目标类别仍具挑战性。为此，小样本目标检测（FSOD）近年来成为热点话题，它模拟人类"学会学习"的能力，智能地将已习得的通用目标知识从常见重尾类别迁移至新型长尾目标类别。近年来，这一新兴领域的研究蓬勃发展，涌现出各类基准、主干网络及方法论。现有FSOD综述文献[58,59,74,78]已从微调/迁移学习与元学习方法两大维度系统研究并比较了相关成果。相比之下，本文从全新视角出发，基于方法论贡献提出新型分类体系（数据导向、模型导向、算法导向），对FSOD最新进展进行包含性能对比的全面综述。此外，我们深入剖析技术挑战、各类方法的优劣，并展望FSOD未来发展方向。具体而言，本文首先概述FSOD的问题定义、通用数据集及评估协议；继而提出三类FSOD方法分类体系并系统梳理研究进展；最后围绕性能表现、技术挑战与未来方向展开深入讨论。