FcaNet: Frequency Channel Attention Networks

Attention mechanism, especially channel attention, has gained great success in the computer vision field. Many works focus on how to design efficient channel attention mechanisms while ignoring a fundamental problem, i.e., using global average pooling (GAP) as the unquestionable pre-processing method.In this work, we start from a different view and rethink channel attention using frequency analysis. Based on the frequency analysis, we mathematically prove that the conventional GAP is a special case of the feature decomposition in the frequency domain. With the proof, we naturally generalize the pre-processing of channel attention mechanism in the frequency domain and propose FcaNet with novel multi-spectral channel attention. The proposed method is simple but effective. We can change only one line of code in the calculation to implement our method within existing channel attention methods. Moreover, the proposed method achieves state-of-the-art results compared with other channel attention methods on image classification, object detection, and instance segmentation tasks. Our method could improve by 1.8% in terms of Top-1 accuracy on ImageNet compared with the baseline SENet-50, with the same number of parameters and the same computational cost. Our code and models will be made publicly available.

FcaNet：频道关注网络

注意力机制，尤其是渠道注意力，在计算机视觉领域取得了巨大的成功。许多工作着重于如何设计有效的频道关注机制，同时忽略一个基本问题，即使用全局平均池（GAP）作为毫无疑问的预处理方法。.. 在这项工作中，我们从不同的角度出发，并使用频率分析重新考虑频道的注意力。基于频率分析，我们在数学上证明了传统的GAP是频域中特征分解的特例。有了证明，我们自然地在频域上概括了信道注意机制的预处理，并提出了具有新颖的多光谱信道注意的FcaNet。所提出的方法简单但有效。我们只能在计算中更改一行代码，以在现有渠道关注方法中实施我们的方法。此外，与其他在图像分类，目标检测和实例分割任务上的通道关注方法相比，该方法获得了最新的结果。我们的方法可以提高1。在相同数量的参数和相同的计算成本的情况下，与基线SENet-50相比，ImageNet的Top-1准确性为8％。我们的代码和模型将公开提供。 （阅读更多）