Recently, few-shot learning has received considerable attention from researchers. Compared to deep learning, which requires abundant data for training, few-shot learning only requires a few labeled samples. Therefore, few-shot learning has been extensively used in scenarios in which a large number of samples cannot be obtained. However, effectively extracting features from a limited number of samples are the most important problem in few-shot learning. To solve this limitation, a multi-local feature relation network (MLFRNet) is proposed to improve the accuracy of few-shot image classification...
