Li Fufang, Zhang Weixiang, Shang Yi
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
Biomimetics (Basel). 2025 Jan 1;10(1):16. doi: 10.3390/biomimetics10010016.
In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot image classification. Few-shot image classification aims to address the problem of image classification when data are limited. Traditional models require a large amount of labeled data for training, while few-shot learning trains models using only a small number of samples (just a few samples per class) to recognize new categories. EMNet shows its potential for bio-inspired algorithms in optimizing feature extraction and enhancing generalization capabilities. It features two key modules: Enhanced Self-Correlated Attention (ESCA) and Multi-Branch Joint Module (MBJ Module). EMNet tackles two main challenges in few-shot learning: how to make an effective important feature extraction and enhancement in images, and improving generalization to new categories. The ESCA module boosts the precision in extracting crucial local features, enhancing classification accuracy. The MBJ module focuses on shared features across images, emphasizing similarities within classes and subtle differences between them. This enhances model adaptability and generalization to new categories. Experimental results show that our model performs better than existing models in one-shot and five-shot tasks on mini-ImageNet, CUB-200, and CIFAR-FS datasets, which proves the proposed model to be an efficient end-to-end solution for few-shot image classification. In the five-way one-shot and five-way five-shot experiments on the CUB-200-2011 dataset, EMNet achieved classification accuracies that were 1.27 and 0.54 percentage points higher than those of RENet, respectively. In the five-way one-shot and five-way five-shot experiments on the miniImageNet dataset, EMNet's classification accuracies were 0.02 and 0.48 percentage points higher than those of RENet, respectively. In the five-way one-shot and five-way five-shot experiments on the CIFAR-FS dataset, EMNet's classification accuracies were 0.19 and 0.18 percentage points higher than those of RENet.
在本研究中，受生物视觉注意力机制原理和自然界中群体智能的启发，我们提出了一种增强自相关注意力与多分支联合模块网络（EMNet），这是一种用于少样本图像分类的新型模型。少样本图像分类旨在解决数据有限时的图像分类问题。传统模型需要大量标注数据进行训练，而少样本学习仅使用少量样本（每个类别仅有几个样本）来训练模型以识别新类别。EMNet展示了其在优化特征提取和增强泛化能力方面对于受生物启发算法的潜力。它具有两个关键模块：增强自相关注意力（ESCA）和多分支联合模块（MBJ模块）。EMNet解决了少样本学习中的两个主要挑战：如何在图像中进行有效的重要特征提取和增强，以及提高对新类别的泛化能力。ESCA模块提高了提取关键局部特征的精度，增强了分类准确性。MBJ模块专注于图像间的共享特征，强调类内的相似性以及它们之间的细微差异。这增强了模型对新类别的适应性和泛化能力。实验结果表明，我们的模型在mini-ImageNet、CUB-200和CIFAR-FS数据集的单样本和五样本任务中表现优于现有模型，这证明了所提出的模型是一种用于少样本图像分类的高效端到端解决方案。在CUB-200-2011数据集的五分类单样本和五分类五样本实验中，EMNet的分类准确率分别比RENet高1.27和0.54个百分点。在miniImageNet数据集的五分类单样本和五分类五样本实验中，EMNet的分类准确率分别比RENet高0.02和0.48个百分点。在CIFAR-FS数据集的五分类单样本和五分类五样本实验中，EMNet的分类准确率分别比RENet高0.19和0.18个百分点。
