  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.,
channel attention mechanism uses scalar to represent channel, which is
difficult due to massive information loss. In this work, we start from a
different view and regard the channel representation problem as a compression
process using frequency analysis. Based on the frequency analysis, we
mathematically prove that the conventional global average pooling is a special
case of the feature decomposition in the frequency domain. With the proof, we
naturally generalize the compression of the channel attention mechanism in the
frequency domain and propose our method with multi-spectral channel attention,
termed as FcaNet. FcaNet is simple but effective. We can change a few lines 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
consistently outperform the baseline SENet, with the same number of parameters
and the same computational cost. Our code and models will are publicly
available at https://github.com/cfzd/FcaNet.
