用傅里叶滤波增强注意力，实现全局感受野的高效图像分类
FwNet-ECA: A Classification Model Enhancing Window Attention with Global Receptive Fields via Fourier Filtering Operations
- 通过傅里叶变换与可学习权重提升频域特征
- 参数量和计算开销低于移位窗口方法，精度相当
- 适合追求高效视觉模型的开发者
窗口注意力机制被引入以缓解全局注意力带来的高计算开销。本文提出FwNet-ECA，利用傅里叶变换与可学习权重矩阵增强图像的频域特征，通过滤波增强建立全局感受野，避免使用移动窗口注意力。同时引入高效通道注意力（ECA）模块，提升通道间通信。本方法不依赖物理滑窗，而是通过频域增强隐式连接空间区域信息。在iCartoonFace数据集上验证，并在ImageNet上进行下游任务，结果表明模型参数更少、计算开销更低，同时保持竞争力的精度。可视化显示，滤波增强在浅层特征图较大时效果更显著。该工作为视觉任务中的注意力机制提供了更高效有效的替代方案。
原文摘要 · Abstract (English)
Windowed attention mechanisms were introduced to mitigate the issue of excessive computation inherent in global attention mechanisms. In this paper, we present FwNet-ECA, a novel method that utilizes Fourier transforms paired with learnable weight matrices to enhance the spectral features of images. This method establishes a global receptive field through Filter Enhancement and avoids the use of moving window attention. Additionally, we incorporate the Efficient Channel Attention (ECA) module to improve communication between different channels. Instead of relying on physically shifted windows, our approach leverages frequency domain enhancement to implicitly bridge information across spatial regions. We validate our model on the iCartoonFace dataset and conduct downstream tasks on ImageNet, demonstrating that our model achieves lower parameter counts and computational overheads compared to shifted window approaches, while maintaining competitive accuracy. Furthermore, our visualization operations clearly demonstrated that the Filter Enhancement technique achieves greater effectiveness in the model's shallow layers, where feature maps are relatively larger. This work offers a more efficient and effective alternative for leveraging attention mechanisms in visual processing tasks, alleviating the challenges associated with windowed attention models. Code is available at https://github.com/qingxiaoli/FwNet-ECA
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