  Recent advances in self-attention and pure multi-layer perceptrons (MLP)
models for vision have shown great potential in achieving promising performance
with fewer inductive biases. These models are generally based on learning
interaction among spatial locations from raw data. The complexity of
self-attention and MLP grows quadratically as the image size increases, which
makes these models hard to scale up when high-resolution features are required.
In this paper, we present the Global Filter Network (GFNet), a conceptually
simple yet computationally efficient architecture, that learns long-term
spatial dependencies in the frequency domain with log-linear complexity. Our
architecture replaces the self-attention layer in vision transformers with
three key operations: a 2D discrete Fourier transform, an element-wise
multiplication between frequency-domain features and learnable global filters,
and a 2D inverse Fourier transform. We exhibit favorable accuracy/complexity
trade-offs of our models on both ImageNet and downstream tasks. Our results
demonstrate that GFNet can be a very competitive alternative to
transformer-style models and CNNs in efficiency, generalization ability and
robustness. Code is available at https://github.com/raoyongming/GFNet
