Few shot image recognition based on convolutional neural network feature extraction and metric learning methods

The development of Deep Learning technology has led to a qualitative leap in image recognition. However, the large-scale annotation of data and high-performance computing requirements limit the application of image recognition methods in practical tasks. The research on few shot image recognition emerged as a result. This article mainly discusses the metric learning methods and three network models based on convolutional neural networks, elaborating on their model structures and algorithm ideas. The performance of few shot image recognition under different network models was compared and analyzed through experiments on different datasets. The summary section provides prospects for future research.