自适应感受野网络提升细粒度少样本图像分类效果
Adaptive receptive field-based spatial-frequency feature reconstruction network for fine-grained few-shot image classification
- 根据输入图像自动调整感受野大小以提取空间与频域特征
- 在CUB、Cars等数据集上显著优于当前最佳方法
- 适合需要高效特征重建的少样本细粒度分类任务
特征重构技术广泛应用于少样本细粒度图像分类（FSFGIC）。研究发现，现有基于特征的FSFGIC方法主要挑战在于如何为不同类别输入图像选择合适的感受野大小，以提取空间和频率特征描述符。为此，提出一种基于自适应感受野的空间-频域特征重构网络（ARF-SFR-Net）。该网络能自适应确定感受野大小，有效提取并融合空间与频率特征，用于特征重构与分类任务。ARF-SFR-Net可轻松嵌入到给定的周期训练机制中，实现从零开始的端到端训练。在多个FSFGIC基准数据集上的大量实验表明，所提方法在性能上显著优于当前最优方法。代码已公开于：https://github.com/ICL-SUST/ARF-SFR-Net。
原文摘要 · Abstract (English)
Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing feature-based FSFGIC methods is how to choose the size of the receptive field to extract feature descriptors (including spatial and frequency feature descriptors) from different category input images, thereby better performing the FSFGIC tasks. To address this, an adaptive receptive field-based spatial-frequency feature reconstruction network (ARF-SFR-Net) is proposed. The designed ARF-SFR-Net has the capability to adaptively determine receptive field sizes for obtaining spatial and frequency features, and effectively fuse them for reconstruction and FSFGIC tasks. The designed ARF-SFR-Net can be easily embedded into a given episodic training mechanism for end-to-end training from scratch. Extensive experiments on multiple FSFGIC benchmarks demonstrate the effectiveness and superiority of the proposed ARF-SFR-Net over state-of-the-art approaches. The code is available at: https://github.com/ICL-SUST/ARF-SFR-Net.
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