LAUSR

Few-shot learning is one of the most challenging problems in computer vision due to the difficulty of sample collection in many real-world applications. It aims at classifying a sample when the number of training samples for each identity is limited. Most of the existing few-shot learning models learn a distance metric with pairwise or triplet constraints. In this paper, we make initial attempts on learning local and global similarities simultaneously to improve the few-shot classification performance in terms of accuracy. In particular, our system differs in two aspects. Firstly, we develop a neural network to learn the pairwise local relationship between each pair of samples in the union set that is composed of support set and query set, which fully utilize the supervision. Secondly, we design a global similarity function from the manifold perspective. The latent assumption is that if the neighbors of one sample are similar to those of another sample, the global similarity between them will be high. Otherwise, the global similarity of the two samples will become very low even if the local similarity between them is high. Meanwhile, we propose a new loss according to the pairwise local loss and task-specific global loss, encouraging the model toward better generalization. Extensive experiments on three popular benchmarks (Omniglot, miniImageNet and tieredImageNet) demonstrate that our simple, yet effective approach can achieve competitive accuracy compared to the state-of-the-art methods.