Vision multi-layer perceptrons (MLPs) have shown promising performance in
computer vision tasks, and become the main competitor of CNNs and vision
Transformers. They use token-mixing layers to capture cross-token interactions,
as opposed to the multi-head self-attention mechanism used by Transformers.
However, the heavily parameterized token-mixing layers naturally lack
mechanisms to capture local information and multi-granular non-local relations,
thus their discriminative power is restrained. To tackle this issue, we propose
a new positional spacial gating unit (PoSGU). It exploits the attention
formulations used in the classical relative positional encoding (RPE), to
efficiently encode the cross-token relations for token mixing. It can
successfully reduce the current quadratic parameter complexity $O(N^2)$ of
vision MLPs to $O(N)$ and $O(1)$. We experiment with two RPE mechanisms, and
further propose a group-wise extension to improve their expressive power with
the accomplishment of multi-granular contexts. These then serve as the key
building blocks of a new type of vision MLP, referred to as PosMLP. We evaluate
the effectiveness of the proposed approach by conducting thorough experiments,
demonstrating an improved or comparable performance with reduced parameter
complexity. For instance, for a model trained on ImageNet1K, we achieve a
performance improvement from 72.14\% to 74.02\% and a learnable parameter
reduction from $19.4M$ to $18.2M$. Code could be found at
this https URL.

该研究提出了一种新的位置空间门控单元（PoSGU），用于处理视觉多层感知器中的局部信息和多粒度非局部关系，从而构建了一种新型的视觉 MLP，称为 PosMLP，减少了参数复杂度而其性能得到提高。