Lanzhou University of Technology Institutional Repository （LUT_IR）
| Dual Feature Reconstruction Network For Few-shot Image Classification | |
| Guo, Xiaowei; Wu, Jijie; Ren, Kai; Song, Qi; Li, Xiaoxu | |
| 2023 | |
| Conference Name | 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 |
| Source Publication | 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
 |
| Pages | 1579-1584 |
| Conference Date | October 31, 2023 - November 3, 2023 |
| Conference Place | Taipei, Taiwan |
| Publication Place | NEW YORK |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Abstract | Few-shot image classification aims to provide accurate predictions for novelty by learning from a limited number of samples. Classical few-shot image classification methods usually use data augmentation and self-supervision to compensate for the lack of training sample, and introduce migration learning and meta-learning to pre-train the model or accelerate the model optimization, which improves the classification performance of the model. However, with a small amount of labeled sample data, these methods cannot meet the requirements of the model's ability to characterize sample features, resulting in a model that is highly susceptible to overfitting problems. In this paper, we propose a Dual Feature Reconstruction Network (DFRN) for few-shot image classification. The network constructs the double feature vector by two modules, in which the first-level feature module generates an attention mask based on the image to make the feature vector characterize more of the target region, and the secondary feature module interferes with the feature vector to improve its generalization performance. In addition, the network also enhances the classification performance of the model by considering the contextual information of the support classes through an auxiliary loss function. Through extensive experiments, the network proposed in this paper achieves excellent performance on Flowers, CUB and Cars datasets and outperforms other reference fine-grained image classification methods such as FRN. © 2023 IEEE. |
| Keyword | Classification (of information) - Computer vision - Image enhancement - Image reconstruction - Learning systems Accurate prediction - Classification methods - Classification performance - Data augmentation - Feature reconstruction - Features vector - Images classification - Number of samples - Reconstruction networks - Training sample |
| DOI | 10.1109/APSIPAASC58517.2023.10317363 |
| Indexed By | EI ; CPCI-S |
| Language | 英语 |
| WOS Research Area | Computer Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods |
| WOS ID | WOS:001108741800247 |
| EI Accession Number | 20235115257000 |
| EI Keywords | Image classification |
| EI Classification Number | 716.1 Information Theory and Signal Processing - 723.2 Data Processing and Image Processing - 723.5 Computer Applications - 741.2 Vision - 903.1 Information Sources and Analysis |
| Original literature type | Conference article (CA) |
| Citation statistics | none
|
| Document Type | 会议论文 |
| Identifier | https://ir.lut.edu.cn/handle/2XXMBERH/169325 |
| Collection | 兰州理工大学 |
| Corresponding Author | Guo, Xiaowei; Wu, Jijie; Ren, Kai; Song, Qi; Li, Xiaoxu |
| Affiliation | Lanzhou University of Technology, Lanzhou, China |
| First Author Affilication | Lanzhou University of Technology |
| Corresponding Author Affilication | Lanzhou University of Technology |
| Recommended Citation GB/T 7714 | Guo, Xiaowei,Wu, Jijie,Ren, Kai,et al. Dual Feature Reconstruction Network For Few-shot Image Classification[C]. NEW YORK:Institute of Electrical and Electronics Engineers Inc.,2023:1579-1584. |
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