  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.
