Few-shot learning aims to learn a classifier with limited training instances to recognize unseen classes in test. Recently, some effective few-shot learning approaches have achieved promising classification performance. However, these approaches do not pay attention to the importance of task-relevant features in image classification. To address this issue, we propose Task-Relevant Graph Metric Learning method that adopts a novel meta-learning framework for transductive inference. Specifically, we first extract task-relevant features by a Squeeze-and-Excitation module. Then learning a graph construction module in order to obtain the manifold structure in the data. Afterward, self-training is utilized to propagate labels from labeled instances to unlabeled test instances. Experiments on benchmark datasets demonstrate that TRGML improves classification performance (4%-5%) over baseline systems on miniImageNet and tieredImageNet.
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