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Few-shot image classification algorithm based on attention mechanism and weight fusion
Aiming at the existing problems of metric-based methods, there are problems such as inadequate feature extraction, inaccurate class feature representation, and single similarity measurement. A new model based on attention mechanism and weight fusion strategy is proposed in this paper. Firstly, the i...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977640/ http://dx.doi.org/10.1186/s44147-023-00186-9 |
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author | Meng, Xiaoxia Wang, Xiaowei Yin, Shoulin Li, Hang |
author_facet | Meng, Xiaoxia Wang, Xiaowei Yin, Shoulin Li, Hang |
author_sort | Meng, Xiaoxia |
collection | PubMed |
description | Aiming at the existing problems of metric-based methods, there are problems such as inadequate feature extraction, inaccurate class feature representation, and single similarity measurement. A new model based on attention mechanism and weight fusion strategy is proposed in this paper. Firstly, the image is passed through the conv4 network with channel attention mechanism and space attention mechanism to obtain the feature map of the image. On this basis, the fusion strategy is used to extract class-level feature representations according to the difference in contributions of different samples to class-level feature representations. Finally, the similarity scores of query set samples are calculated through the network to predict the classification. Experimental results on the miniImageNet dataset and the omniglot dataset demonstrate the effectiveness of the proposed method. |
format | Online Article Text |
id | pubmed-9977640 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-99776402023-03-02 Few-shot image classification algorithm based on attention mechanism and weight fusion Meng, Xiaoxia Wang, Xiaowei Yin, Shoulin Li, Hang J. Eng. Appl. Sci. Research Aiming at the existing problems of metric-based methods, there are problems such as inadequate feature extraction, inaccurate class feature representation, and single similarity measurement. A new model based on attention mechanism and weight fusion strategy is proposed in this paper. Firstly, the image is passed through the conv4 network with channel attention mechanism and space attention mechanism to obtain the feature map of the image. On this basis, the fusion strategy is used to extract class-level feature representations according to the difference in contributions of different samples to class-level feature representations. Finally, the similarity scores of query set samples are calculated through the network to predict the classification. Experimental results on the miniImageNet dataset and the omniglot dataset demonstrate the effectiveness of the proposed method. Springer Berlin Heidelberg 2023-03-02 2023 /pmc/articles/PMC9977640/ http://dx.doi.org/10.1186/s44147-023-00186-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Meng, Xiaoxia Wang, Xiaowei Yin, Shoulin Li, Hang Few-shot image classification algorithm based on attention mechanism and weight fusion |
title | Few-shot image classification algorithm based on attention mechanism and weight fusion |
title_full | Few-shot image classification algorithm based on attention mechanism and weight fusion |
title_fullStr | Few-shot image classification algorithm based on attention mechanism and weight fusion |
title_full_unstemmed | Few-shot image classification algorithm based on attention mechanism and weight fusion |
title_short | Few-shot image classification algorithm based on attention mechanism and weight fusion |
title_sort | few-shot image classification algorithm based on attention mechanism and weight fusion |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977640/ http://dx.doi.org/10.1186/s44147-023-00186-9 |
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