  In deep learning, Multi-Layer Perceptrons (MLPs) have once again garnered
attention from researchers. This paper introduces MC-MLP, a general MLP-like
backbone for computer vision that is composed of a series of fully-connected
(FC) layers. In MC-MLP, we propose that the same semantic information has
varying levels of difficulty in learning, depending on the coordinate frame of
features. To address this, we perform an orthogonal transform on the feature
information, equivalent to changing the coordinate frame of features. Through
this design, MC-MLP is equipped with multi-coordinate frame receptive fields
and the ability to learn information across different coordinate frames.
Experiments demonstrate that MC-MLP outperforms most MLPs in image
classification tasks, achieving better performance at the same parameter level.
The code will be available at: https://github.com/ZZM11/MC-MLP.
