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Multi-scale kronecker-product relation networks for few-shot learning

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Abstract

Few-shot learning aims to train classifiers to learn new visual object categories from few training examples. Recently, metric-learning based methods have made promising progress. Relation Network is a metric-based method that uses simple convolutional neural networks to learn deep relationships between image features in order to recognize new objects. However, during the feature comparing phase, Relation Network is considered sensitive to the spatial positions of the compared objects. Moreover, it learns from only single-scale features which can lead to a poor generalization ability due to scale variation of the compared objects. To solve these problems, we intend to extend Relation Network to be position-aware and integrate multi-scale features for more robust metric learning and better generalization ability. In this paper, we propose a novel few-shot learning method called Multi-scale Kronecker-Product Relation Networks For Few-Shot Learning (MsKPRN). Our method combines feature maps with spatial correlation maps generated from a Kronecker-product module to capture position-wise correlations between the compared features and then feeds them to a relation network module, which captures similarities between the combined features in a multi-scale manner. Extensive experiments demonstrate that the proposed method outperforms the related state-of-the-art methods on popular few-shot learning datasets. Particularly, MsKPRN has improved the accuracy of Relation Network from 50.44 to 57.02 and from 65.63 to 72.06 on 5-way 1-shot and 5-shot scenarios, respectively. Our code will be available on: https://github.com/mouniraziz/MsKPRN.

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Acknowledgements

We would like to thank the anonymous referees for their helpful comments and suggestions.

Funding

This study was funded by the National Natural Science Foundation of China (Grant No.61379109,M1321007) and Science and Technology Plan of Hunan Province (Grant No.2014GK2018, 2016JC2011).

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  1. School of Computer Science & Engineering, Central South University, 932 South Lushan Rd, 410083, Changsha, Hunan, People’s Republic of China

    Mounir Abdelaziz & Zuping Zhang

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  1. Mounir Abdelaziz
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Correspondence to Zuping Zhang.

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Abdelaziz, M., Zhang, Z. Multi-scale kronecker-product relation networks for few-shot learning. Multimed Tools Appl 81, 6703–6722 (2022). https://doi.org/10.1007/s11042-021-11735-w

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