计算机技术与发展2024，Vol.34Issue(1)：44-51,8.DOI:10.3969/j.issn.1673-629X.2024.01.007
基于判别性特征增强的小样本细粒度图像识别
Few-shot Fine-grained Image Recognition Based on Discriminative Feature Enhancement
摘要
Abstract
Few-shot fine-grained image recognition is a popular research topic in the field of deep learning.Its basic task is to identify images of subcategories under a super class while learning a limited number of samples.Thanks to the rapid development of convolutional neural networks,the accuracy of few-shot fine-grained image recognition has achieved remarkable results,but its performance is still limited by the high variance among images of the same subclass and the variability of discriminative features in different classification tasks.To address the above problems,we propose a few-shot fine-grained image recognition algorithm(DFENet)based on discriminative feature enhancement.DFENet is designed with a symmetric attention module to enhance intra-class visual consistency learning,thus reducing the influence of background and increasing the weight of feature representations shared among similar samples.In addition,DFENet introduces a discriminative feature enhancement module of channel dimension,and further mines discriminative features suitable for the current task by exploiting the channel relationships within similar samples and between samples of different classes in the support set to improve the recognition accuracy.Extensive experiments are conducted on three classical fine-grained datasets CUB-200-2011,Stanford Dogs,and Stanford Cars.It is showed that the proposed method all achieves competitive results.关键词
小样本细粒度图像识别/深度学习/特征增强/注意力机制/视觉一致性Key words
few-shot fine-grained image recognition/deep learning/feature enhancement/attention mechanism/visual consistency分类
信息技术与安全科学引用本文复制引用
齐妍,孙涵..基于判别性特征增强的小样本细粒度图像识别[J].计算机技术与发展,2024,34(1):44-51,8.基金项目
中央高校基本科研业务费专项资金资助项目(NZ2019009) （NZ2019009）
