We present SplitMixer, a simple and lightweight isotropic MLP-like
architecture, for visual recognition. It contains two types of interleaving
convolutional operations to mix information across spatial locations (spatial
mixing) and channels (channel mixing). The first one includes sequentially
applying two depthwise 1D kernels, instead of a 2D kernel, to mix spatial
information. The second one is splitting the channels into overlapping or
non-overlapping segments, with or without shared parameters, and applying our
proposed channel mixing approaches or 3D convolution to mix channel
information. Depending on design choices, a number of SplitMixer variants can
be constructed to balance accuracy, the number of parameters, and speed. We
show, both theoretically and experimentally, that SplitMixer performs on par
with the state-of-the-art MLP-like models while having a significantly lower
number of parameters and FLOPS. For example, without strong data augmentation
and optimization, SplitMixer achieves around 94% accuracy on CIFAR-10 with only
0.28M parameters, while ConvMixer achieves the same accuracy with about 0.6M
parameters. The well-known MLP-Mixer achieves 85.45% with 17.1M parameters. On
CIFAR-100 dataset, SplitMixer achieves around 73% accuracy, on par with
ConvMixer, but with about 52% fewer parameters and FLOPS. We hope that our
results spark further research towards finding more efficient vision
architectures and facilitate the development of MLP-like models. Code is
available at this https URL.

SplitMixer 是一个简单且轻量级的 MLP-like 架构，包含用于混合空间位置和通道信息的两种交替卷积操作，理论和实验证明它在准确性，参数数量和速度方面都表现出色。

SplitMixer: MLP 模型中的脂肪裁剪

SplitMixer: Fat Trimmed From MLP-like Models

This paper presents a simple MLP-like architecture, CycleMLP, which is a
versatile backbone for visual recognition and dense predictions. As compared to
modern MLP architectures, e.g., MLP-Mixer, ResMLP, and gMLP, whose
architectures are correlated to image size and thus are infeasible in object
detection and segmentation, CycleMLP has two advantages compared to modern
approaches. (1) It can cope with various image sizes. (2) It achieves linear
computational complexity to image size by using local windows. In contrast,
previous MLPs have $O(N^2)$ computations due to fully spatial connections. We
build a family of models which surpass existing MLPs and even state-of-the-art
Transformer-based models, e.g., Swin Transformer, while using fewer parameters
and FLOPs. We expand the MLP-like models' applicability, making them a
versatile backbone for dense prediction tasks. CycleMLP achieves competitive
results on object detection, instance segmentation, and semantic segmentation.
In particular, CycleMLP-Tiny outperforms Swin-Tiny by 1.3% mIoU on ADE20K
dataset with fewer FLOPs. Moreover, CycleMLP also shows excellent zero-shot
robustness on ImageNet-C dataset. Code is available at
this https URL

这篇论文介绍了一种简单的 MLP 类型架构 CycleMLP 对于视觉识别和密集预测是一种通用的骨干，优于现代 MLP 架构，可适应各种图像尺寸，能够实现与图像大小的线性计算复杂度， 用局部窗口代替全空间连接，成功地应用于物体检测、实例分割和语义分割等领域，表现优异，比当前其他已知的 MLP 和变压器模型更具优势。

CycleMLP: 密集预测的类 MLP 架构

CycleMLP: A MLP-like Architecture for Dense Prediction

In this paper, we present Vision Permutator, a conceptually simple and data
efficient MLP-like architecture for visual recognition. By realizing the
importance of the positional information carried by 2D feature representations,
unlike recent MLP-like models that encode the spatial information along the
flattened spatial dimensions, Vision Permutator separately encodes the feature
representations along the height and width dimensions with linear projections.
This allows Vision Permutator to capture long-range dependencies along one
spatial direction and meanwhile preserve precise positional information along
the other direction. The resulting position-sensitive outputs are then
aggregated in a mutually complementing manner to form expressive
representations of the objects of interest. We show that our Vision Permutators
are formidable competitors to convolutional neural networks (CNNs) and vision
transformers. Without the dependence on spatial convolutions or attention
mechanisms, Vision Permutator achieves 81.5% top-1 accuracy on ImageNet without
extra large-scale training data (e.g., ImageNet-22k) using only 25M learnable
parameters, which is much better than most CNNs and vision transformers under
the same model size constraint. When scaling up to 88M, it attains 83.2% top-1
accuracy. We hope this work could encourage research on rethinking the way of
encoding spatial information and facilitate the development of MLP-like models.
Code is available at this https URL

本文提出 Vision Permutator，这是一种类似于 MLP 的架构，用于视觉识别。通过在高度和宽度维度上分别编码特征表示，Vision Permutator 能够捕捉沿一个空间方向的长程依赖关系，同时保留另一个方向上的精确位置信息。在 ImageNet 数据集上，使用仅有 25M 可学习参数的 Vision Permutator 在不使用大规模训练数据（如 ImageNet-22k）的情况下可实现 81.5％的 top-1 准确性，比大多数 CNN 和视觉变换器都要好。