Problem
Research questions and friction points this paper is trying to address.
phase information
few-shot fine-grained image classification
structural relationships
The impact of phase information for few-shot fine-grained image classification
🤖 AI Summary
本文通过引入一种新的幅度-相位整合模块和PSF-Net网络，利用相位信息改善少量样本细粒度图像分类问题，实验表明该方法优于现有技术。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
phase information
amplitude-phase integration (API) module
PSF-Net
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