Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification
In spite of recent rapid developments across various computer vision domains, numerous cutting-edge deep learning algorithms often demand a substantial volume of data to operate effectively. Within this research, a novel few-shot learning approach is presented with the objective of enhancing the acc...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/19/10996 |
| _version_ | 1797576216012652544 |
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| author | Yaoqun Xu Yuemao Wang |
| author_facet | Yaoqun Xu Yuemao Wang |
| author_sort | Yaoqun Xu |
| collection | DOAJ |
| description | In spite of recent rapid developments across various computer vision domains, numerous cutting-edge deep learning algorithms often demand a substantial volume of data to operate effectively. Within this research, a novel few-shot learning approach is presented with the objective of enhancing the accuracy of few-shot image classification. This task entails the classification of unlabeled query samples based on a limited set of labeled support examples. Specifically, the integration of the edge-conditioned graph neural network (EGNN) framework with hierarchical node residual connections is proposed. The primary aim is to enhance the performance of graph neural networks when applied to few-shot classification, a rather unconventional application of hierarchical node residual structures in few-shot image classification tasks. It is noteworthy that this work represents an innovative attempt to combine these two techniques. Extensive experimental findings on publicly available datasets demonstrate that the methodology surpasses the original EGNN algorithm, achieving a maximum improvement of 2.7%. Particularly significant is the performance gain observed on our custom-built dataset, CBAC (Car Brand Appearance Classification), which consistently outperforms the original method, reaching an impressive peak improvement of 11.14%. |
| first_indexed | 2024-03-10T21:49:07Z |
| format | Article |
| id | doaj.art-ef5089114dd84f9b93467ec73ef4a298 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:49:07Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ef5089114dd84f9b93467ec73ef4a2982023-11-19T14:07:09ZengMDPI AGApplied Sciences2076-34172023-10-0113191099610.3390/app131910996Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image ClassificationYaoqun Xu0Yuemao Wang1Institute of System Engineering, Harbin University of Commerce, Harbin 150028, ChinaSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, ChinaIn spite of recent rapid developments across various computer vision domains, numerous cutting-edge deep learning algorithms often demand a substantial volume of data to operate effectively. Within this research, a novel few-shot learning approach is presented with the objective of enhancing the accuracy of few-shot image classification. This task entails the classification of unlabeled query samples based on a limited set of labeled support examples. Specifically, the integration of the edge-conditioned graph neural network (EGNN) framework with hierarchical node residual connections is proposed. The primary aim is to enhance the performance of graph neural networks when applied to few-shot classification, a rather unconventional application of hierarchical node residual structures in few-shot image classification tasks. It is noteworthy that this work represents an innovative attempt to combine these two techniques. Extensive experimental findings on publicly available datasets demonstrate that the methodology surpasses the original EGNN algorithm, achieving a maximum improvement of 2.7%. Particularly significant is the performance gain observed on our custom-built dataset, CBAC (Car Brand Appearance Classification), which consistently outperforms the original method, reaching an impressive peak improvement of 11.14%.https://www.mdpi.com/2076-3417/13/19/10996Graph Neural Networks (GNNs)residual structurefew-shot learningimage classification |
| spellingShingle | Yaoqun Xu Yuemao Wang Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification Applied Sciences Graph Neural Networks (GNNs) residual structure few-shot learning image classification |
| title | Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification |
| title_full | Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification |
| title_fullStr | Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification |
| title_full_unstemmed | Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification |
| title_short | Fused Node-Level Residual Structure Edge Graph Neural Network for Few-Shot Image Classification |
| title_sort | fused node level residual structure edge graph neural network for few shot image classification |
| topic | Graph Neural Networks (GNNs) residual structure few-shot learning image classification |
| url | https://www.mdpi.com/2076-3417/13/19/10996 |
| work_keys_str_mv | AT yaoqunxu fusednodelevelresidualstructureedgegraphneuralnetworkforfewshotimageclassification AT yuemaowang fusednodelevelresidualstructureedgegraphneuralnetworkforfewshotimageclassification |