Infrared aircraft few-shot classification method based on meta learning

1.Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China;2.University of Chinese Academy of Sciences, Beijing 100049, China;3.Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

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Supported by Key Laboratory of Shanghai Field Physical Evidence Foundation (2017xcwzk08)

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    Aiming at the problem of insufficient samples of infrared aircrafts and low accuracy of fine-grained classification, a method of infrared aircraft few-shot classification based on meta learning is proposed. Based on meta learning and combined with multi-scale feature fusion, this method can effectively extract commonness among different classification tasks while reducing computation, and then classify different tasks with fine-tuning. The experiments proved that this method could improve the classification accuracy of mini-ImageNet dataset while reducing the calculation amount by about 70%. The accuracy of fine-grained classification for infrared aircrafts with few samples reached 92.74%.

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CHEN Rui-Min, LIU Shi-Jian, MIAO Zhuang, LI Fan-Ming. Infrared aircraft few-shot classification method based on meta learning[J]. Journal of Infrared and Millimeter Waves,2021,40(4):554~560

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  • Received:July 28,2020
  • Revised:July 29,2021
  • Adopted:November 18,2020
  • Online: July 28,2021
  • Published: