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A multi-scale hierarchical node graph neural network for few-shot learning

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Abstract

The graph neural network model has shown strong classification performance in few-shot learning (FSL) tasks in recent years. However, the existing methods have mainly participated in model training by extracting the single features of the samples as nodes. This method of constructing nodes ignores the multi-scale information contained in the samples to a certain extent and limits the expressiveness of node features. Therefore, we propose a multi-scale hierarchical node graph neural network (MsHN-GNN), which constructs hierarchical nodes bearing multi-scale information by deeply extracting structural features, global features, and local features of samples and enhances the representation ability of node features. At the same time, MsHN-GNN sorts the hierarchical relationship among sub-nodes through the block alignment module so that sub-nodes correspond one-to-one according to their own content attributes, which lays a foundation for updating multi-scale hierarchical nodes. In addition, we design a unique graph network update method according to the characteristics of hierarchical nodes. Through the multi-scale hierarchical node aggregation and edge aggregation modules, we realize the hierarchical information interaction among multiple sub-nodes so that the updated nodes and edge features have stronger information expression ability. Finally, to verify the effectiveness of the model, we conduct extensive experiments on miniImageNet, TiredImageNet, and CUB-200-2011 datasets. The experimental results show that MsHN-GNN outperforms other advanced few-shot learning methods with a graph structure in classification performance. In the above three datasets, the 5-way 1-shot experimental results achieved 68.32%, 72.56%, and 76.21%, respectively.

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Data Availability

The miniImageNet dataset is available at https://image-net.org/, Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, 2016. Matching networks for one shot learning, in: Advances in Neural Information Processng Systems, pp. 3630-3638. [18] The TieredImageNet dataset is available at https://image-net.org/, Ren M, Triantafillou E, Ravi S, et al. Meta-learning for semi-supervised few-shot classification[J]. arXiv preprint arXiv:1803.00676, 2018. The CUB-200-2011 dataset is available at http://www.vision.caltech.edu/visipedia/CUB-200-2011.html, Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds- 200-2011 dataset. 2011. [32]

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Acknowledgements

This work was supported by Anhui Province Key Research and Development Program (2022k07020006), the central government guides local funds of Anhui provincial in 2021 (K120636001) and the University Synergy Innovation Program of Anhui Province(GXXT-2022-038).

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Anhui University, Hefei, Anhui, 230001, China

    Yan Zhang, Xudong Zhou, Nian Wang & Zenghui Li

  2. School of Internet, Anhui University, Hefei, Anhui, 230001, China

    Ke Wang

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  1. Yan Zhang
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  2. Xudong Zhou
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  3. Ke Wang
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  4. Nian Wang
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  5. Zenghui Li
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Yan Zhang], [Xudong Zhou], [Ke Wang], [Nian Wang] and [Zenghui Li]. The first draft of the manuscript was written by [Xudong Zhou] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Nian Wang.

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Zhang, Y., Zhou, X., Wang, K. et al. A multi-scale hierarchical node graph neural network for few-shot learning. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17059-1

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