  Multi-head-self-attention (MHSA)-equipped models have achieved notable
performance in computer vision. Their computational complexity is proportional
to quadratic numbers of pixels in input feature maps, resulting in slow
processing, especially when dealing with high-resolution images. New types of
token-mixer are proposed as an alternative to MHSA to circumvent this problem:
an FFT-based token-mixer involves global operations similar to MHSA but with
lower computational complexity. However, despite its attractive properties, the
FFT-based token-mixer has not been carefully examined in terms of its
compatibility with the rapidly evolving MetaFormer architecture. Here, we
propose a novel token-mixer called Dynamic Filter and novel image recognition
models, DFFormer and CDFFormer, to close the gaps above. The results of image
classification and downstream tasks, analysis, and visualization show that our
models are helpful. Notably, their throughput and memory efficiency when
dealing with high-resolution image recognition is remarkable. Our results
indicate that Dynamic Filter is one of the token-mixer options that should be
seriously considered. The code is available at
https://github.com/okojoalg/dfformer
