利用相位信息提升少样本细粒度图像分类性能
The impact of phase information for few-shot fine-grained image classification
- 提出相位-振幅融合模块，捕捉图像结构关系
- 在5个公开数据集上超越现有最先进方法
- 模块可即插即用，适合细粒度识别研究者
少样本细粒度图像分类（FSFGIC）旨在用极少标注样本区分相似图像。本文强调了相位信息在刻画图像内部结构关系中的关键作用，提出一种新颖的即插即用式振幅-相位融合（API）模块，有效整合局部与全局频率域的振幅和相位信息，生成更全面的特征描述符。同时设计了专门的网络结构PSF-Net，自适应融合基于相位的空间与频域信息，可在标准周期训练架构中端到端训练。在五个公开数据集上的大量实验表明，该方法显著优于现有最先进基准。
原文摘要 · Abstract (English)
Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critical yet underutilized role of phase information in capturing structural relationships within an image. This study introduces a novel plug-and-play amplitude-phase integration (API) module that effectively combines local and global frequency amplitude and phase information for obtaining more comprehensive feature descriptors. Additionally, a dedicated network, named PSF-Net, is proposed that adaptively fuses phase-based spatial and frequency information for FSFGIS. The designed PSF-Net can be easily integrated into standard episodic training architectures for end-to-end training from scratch. Extensive experiments on five public datasets demonstrate that the method outperforms existing state-of-the-art benchmarks.
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