融合频域与空间域信息，提升小样本分类准确率
Spatial frequency information fusion network for few-shot learning
- 提出SFIFNet，通过频域与空间域特征融合增强表征
- 在标准小样本数据集上分类准确率显著提升
- 适合需要高效利用少量图像的视觉识别场景
小样本学习的目标是充分挖掘有限数据中的潜在关联，通过算法训练出性能优异的模型以满足实际应用需求。在实际应用中，每类图像数量通常远少于传统深度学习，易导致过拟合和泛化能力差。现有小样本分类模型多关注空间域信息，忽视了包含更多特征信息的频域信息，忽略频域会限制模型对特征的充分挖掘，影响分类性能。本文基于常规数据增强，提出SFIFNet，创新性地引入数据预处理方法，通过融合频域与空间域信息，提升图像特征表示精度。实验结果证明该方法有效提升了分类性能。
原文摘要 · Abstract (English)
The objective of Few-shot learning is to fully leverage the limited data resources for exploring the latent correlations within the data by applying algorithms and training a model with outstanding performance that can adequately meet the demands of practical applications. In practical applications, the number of images in each category is usually less than that in traditional deep learning, which can lead to over-fitting and poor generalization performance. Currently, many Few-shot classification models pay more attention to spatial domain information while neglecting frequency domain information, which contains more feature information. Ignoring frequency domain information will prevent the model from fully exploiting feature information, which would effect the classification performance. Based on conventional data augmentation, this paper proposes an SFIFNet with innovative data preprocessing. The key of this method is enhancing the accuracy of image feature representation by integrating frequency domain information with spatial domain information. The experimental results demonstrate the effectiveness of this method in enhancing classification performance.
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