Task-relevant graph to propagate labels for few-shot classification

Minjie Zhang; Hui Yan

doi:10.1117/12.2600836

30 June 2021 Task-relevant graph to propagate labels for few-shot classification

Minjie Zhang, Hui Yan

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780W (2021) https://doi.org/10.1117/12.2600836
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

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.

Citation Download Citation

Minjie Zhang and Hui Yan "Task-relevant graph to propagate labels for few-shot classification", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780W (30 June 2021); https://doi.org/10.1117/12.2600836

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