Restore local descriptors network for few-shot learning

Aiming at the problem that the existing few-shot metric learning methods based on local descriptors fail to consider the correlation between local descriptors and fail to make full use of the global feature information of categories, this paper proposes the Restore Local Descriptors Network (RLDN in short). The adjacent GCN module increases the relevance between local descriptors by using the spatial position relation in the same image. The global feature extraction module outputs the global descriptors of the category by learning and fusing the global features of the image, and then concatenates the local descriptors for further restoration. In addition, a new hybrid loss function is proposed by introducing the triple loss which is integrated into the traditional cross entropy loss. It increases the distance between different categories and helps the classifier to reduce the misclassification. The experimental results show that compared with the traditional local descriptor methods, the Restore Local Descriptors Network can reduce the interference of noise features on the classifier and effectively improve the classification accuracy of the model.