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Research on object detection has developed rapidly in recent years with the progress of deep learning. However,the deep learning based object detection systems rely heavily on large scale labeled training data, which are rarely available in our realistic scene, so few-shot object detection get researchers' great concern.In this paper, we present a survey of few-shot object detection, and introduce the mainstream approaches and their characteristics, merits as well as limits.Finally, we provide the possible direction for further few-shot object detection research. [ABSTRACT FROM AUTHOR]