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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2020, Vol. 41 ›› Issue (S1): 723756-723756.doi: 10.7527/S1000-6893.2019.23756

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A survey of few shot learning based on intelligent perception

SONG Chuang1, ZHAO Jiajia1, WANG Kang2, LIANG Xinkai1   

  1. 1. Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing 100074, China;
    2. School of Computer and Technique, Fudan University, Shanghai 200433, China
  • Received:2019-12-13 Revised:2019-12-20 Online:2020-06-30 Published:2019-12-26
  • Supported by:
    Defense Industrial Technology Development Program (JCKY2017204B064)

Abstract: Few-shot learning refers to using only a small amount of supervision information of the target class to train the machine learning model. Due to its practical values, recent advances in few-shot learning by academia and industry have made significant contributions. However, there were few reviews on this issue in China. This paper systematically summarizes and explores the few-shot learning algorithms and the object detection algorithms based on few-shot learning. Firstly, the problem definition of few-shot learning is given, and its connections with other classic machine learning problems are also enumerated. Meanwhile, the theoretical challenges of the problem of few-shot learning are explained. Then, we summarize the image classification based on few-shot learning, and analyze its representative works. Based on this, we focus on the problem of few-shot object detection, especially the problem of zero-shot object detection, and analyze the existing research works in detail. Finally, we look forward to the future development of few-shot learning in terms of problem setting, theoretical research, implementation technology, and application scenarios based on the advantages and disadvantages of the existing methods. It is expected to provide inspirations for the subsequent research works in this field.

Key words: few-shot learning, machine learning, image classification, object detection, zero-shot learning

CLC Number: