  For the past ten years, CNN has reigned supreme in the world of computer
vision, but recently, Transformer has been on the rise. However, the quadratic
computational cost of self-attention has become a serious problem in practice
applications. There has been much research on architectures without CNN and
self-attention in this context. In particular, MLP-Mixer is a simple
architecture designed using MLPs and hit an accuracy comparable to the Vision
Transformer. However, the only inductive bias in this architecture is the
embedding of tokens. This leaves open the possibility of incorporating a
non-convolutional (or non-local) inductive bias into the architecture, so we
used two simple ideas to incorporate inductive bias into the MLP-Mixer while
taking advantage of its ability to capture global correlations. A way is to
divide the token-mixing block vertically and horizontally. Another way is to
make spatial correlations denser among some channels of token-mixing. With this
approach, we were able to improve the accuracy of the MLP-Mixer while reducing
its parameters and computational complexity. The small model that is RaftMLP-S
is comparable to the state-of-the-art global MLP-based model in terms of
parameters and efficiency per calculation. In addition, we tackled the problem
of fixed input image resolution for global MLP-based models by utilizing
bicubic interpolation. We demonstrated that these models could be applied as
the backbone of architectures for downstream tasks such as object detection.
However, it did not have significant performance and mentioned the need for
MLP-specific architectures for downstream tasks for global MLP-based models.
The source code in PyTorch version is available at
\url{https://github.com/okojoalg/raft-mlp}.
