| ZHAO Wencang (赵文仓),QIN Wenqian,LI Ming.[J].高技术通讯(英文),2022,28(3):295~306 |
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| Multi-attention fusion and weighted class representation for few-shot classification |
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| DOI：10.3772/j.issn.1006-6748.2022.03.009 |
| 中文关键词: |
| 英文关键词: few-shot learning (FSL), image classification, metric-learning, multi-attention fusion |
| 基金项目: |
| Author Name | Affiliation | | ZHAO Wencang (赵文仓) | (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, P.R.China) | | QIN Wenqian | (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, P.R.China) | | LI Ming | (College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| The existing few-shot learning (FSL) approaches based on metric-learning usually lack attention to the distinction of feature contributions, and the importance of each sample is often ignored when obtaining the class representation, where the performance of the model is limited. Additionally, similarity metric method is also worthy of attention. Therefore, a few-shot learning approach called MWNet based on multi-attention fusion and weighted class representation (WCR) is proposed in this paper. Firstly, a multi-attention fusion module is introduced into the model to highlight the valuable part of the feature and reduce the interference of irrelevant content. Then, when obtaining the class representation, weight is given to each support set sample, and the weighted class representation is used to better express the class. Moreover, a mutual similarity metric method is used to obtain a more accurate similarity relationship through the mutual similarity for each representation. Experiments prove that the approach in this paper performs well in few-shot image classification, and also shows remarkable excellence and competitiveness compared with related advanced techniques. |
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