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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.