Token interaction operation is one of the core modules in MLP-based models to
exchange and aggregate information between different spatial locations.
However, the power of token interaction on the spatial dimension is highly
dependent on the spatial resolution of the feature maps, which limits the
model's expressive ability, especially in deep layers where the feature are
down-sampled to a small spatial size. To address this issue, we present a novel
method called \textbf{Strip-MLP} to enrich the token interaction power in three
ways. Firstly, we introduce a new MLP paradigm called Strip MLP layer that
allows the token to interact with other tokens in a cross-strip manner,
enabling the tokens in a row (or column) to contribute to the information
aggregations in adjacent but different strips of rows (or columns). Secondly, a
\textbf{C}ascade \textbf{G}roup \textbf{S}trip \textbf{M}ixing \textbf{M}odule
(CGSMM) is proposed to overcome the performance degradation caused by small
spatial feature size. The module allows tokens to interact more effectively in
the manners of within-patch and cross-patch, which is independent to the
feature spatial size. Finally, based on the Strip MLP layer, we propose a novel
\textbf{L}ocal \textbf{S}trip \textbf{M}ixing \textbf{M}odule (LSMM) to boost
the token interaction power in the local region. Extensive experiments
demonstrate that Strip-MLP significantly improves the performance of MLP-based
models on small datasets and obtains comparable or even better results on
ImageNet. In particular, Strip-MLP models achieve higher average Top-1 accuracy
than existing MLP-based models by +2.44\% on Caltech-101 and +2.16\% on
CIFAR-100. The source codes will be available
at~\href{https://github.com/Med-Process/Strip_MLP{this https URL}.

提出了一种名为 Strip-MLP 的新方法，通过引入 Strip MLP 层、CGSMM 模块和 LSMM 模块，增强了令牌交互的能力，改善了深层次特征的空间表达能力，并在小型数据集上显著提高了 MLP 模型的性能。