Deep neural networks have dominated image recognition task with large amounts of labeled data. But training a well-performing network on a smaller dataset is still a very challenging task. How to learn from limited labeled data is a key research with excellent scenarios and potential applications. There are many ways to solve few-shot recognition problem, but there is still a problem of low recognition accuracy. The fundamental reason is that in few-shot learning, the traditional neural network can only accept a small amount of labeled data, which makes the network unable to obtain enough information for identification. Therefore, the paper proposes a few-shot classification model based on attention mechanism and graph convolutional neural network, which can not only extract features better, but also make full use of the features to classify the target image. Through the attention mechanism, it can guide the neural network to pay attention to more useful information, and graph convolution enables the network to make more accurate judgments by using the information from other classes of support set. Through many experiments, it is proved that the classification accuracy of the model on the Omniglot dataset and the miniImageNet dataset surpasses the original relational network which based on traditional neural network.
