Awsome-Visual-MLPs

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Awsome-Visual-MLPs

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Collect some MLPs with Computer-Vision (CV) papers. If you find some ignored papers, please open issues or pull requests.

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# Awsome-Vision-MLPs
Collecting some MLPs with Computer-Vision (CV) papers.  If you find some ignored papers, please open issues or pull requests.

## Papers

### Transformer original paper

- [Attention is All You Need](https://arxiv.org/abs/1706.03762) (NIPS 2017)

- [Attention is not all you need: pure attention loses rank doubly exponentially with depth](https://arxiv.org/abs/2103.03404) -2021.05.05

### Technical blog

- [Chinese Blog] 3W字长文带你轻松入门视觉transformer [[Link](https://zhuanlan.zhihu.com/p/308301901)]
- [Chinese Blog] Vision Transformer , Vision MLP超详细解读 (原理分析+代码解读) (目录) [[Link](https://zhuanlan.zhihu.com/p/348593638)]

### Survey
  - Transformers in Vision: A Survey [[paper](https://arxiv.org/abs/2101.01169)]   - 2021.02.22
  - A Survey on Visual Transformer [[paper](https://arxiv.org/abs/2012.12556)]   - 2020.1.30

### arXiv papers
- Are we ready for a new paradigm shift? A Survey on Visual Deep MLP[[paper]](https://arxiv.org/abs/2111.04060)
- An Image Patch is a Wave: Phase-Aware Vision MLP[[paper]](https://arxiv.org/abs/2111.12294)
- MorphMLP: A Self-Attention Free, MLP-Like Backbone for Image and Video[[paper]](https://arxiv.org/abs/2111.12527)
- Sparse MLP for Image Recognition: Is Self-Attention Really Necessary?[[paper]](https://arxiv.org/abs/2109.05422)
- ConvMLP: Hierarchical Convolutional MLPs for Vision [[paper]](https://arxiv.org/abs/2109.04454) [[code](https://github.com/SHI-Labs/Convolutional-MLPs)]
- Sparse-MLP: A Fully-MLP Architecture with Conditional Computation[[paper]](https://arxiv.org/abs/2109.02008)
- Hire-MLP: Vision MLP via Hierarchical Rearrangement[[paper]](https://arxiv.org/abs/2108.13341)
- RaftMLP: Do MLP-based Models Dream of Winning Over Computer Vision? [[paper]](https://arxiv.org/abs/2108.04384)
- S2-MLPv2: Improved Spatial-Shift MLP Architecture for Vision [[paper]](https://arxiv.org/abs/2108.01072)
- CycleMLP: A MLP-like Architecture for Dense Prediction [[paper]](https://arxiv.org/abs/2107.10224) [[code](https://github.com/ShoufaChen/CycleMLP)]
- AS-MLP: AN AXIAL SHIFTED MLP ARCHITECTURE FOR VISION [[paper]](https://arxiv.org/abs/2107.08391) [[code](https://github.com/svip-lab/AS-MLP)]
- Global Filter Networks for Image Classification [[paper]](https://arxiv.org/abs/2107.00645) [[code](https://github.com/raoyongming/GFNet)]
- What Makes for Hierarchical Vision Transformer? [[paper]](https://arxiv.org/abs/2107.02174).
- Rethinking Token-Mixing MLP for MLP-based Vision Backbone [[paper]](https://arxiv.org/abs/2106.14882).
- **[Vision Permutator]** Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition [[paper]](https://arxiv.org/abs/2106.12368) [[code](https://github.com/Andrew-Qibin/VisionPermutator)]
- **[S2-MLP]** S2-MLP: Spatial-Shift MLP Architecture for Vision [[paper](https://arxiv.org/abs/2106.07477)] 
- **[Graph-MLP]** Graph-MLP: Node Classification without Message Passing in Graph [[paper](https://arxiv.org/abs/2106.04051)] 
- When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations [[paper](https://arxiv.org/abs/2106.01548)] 
- **[Container]** Container: Context Aggregation Network [[paper](https://arxiv.org/abs/2106.01401)] 
- Can Attention Enable MLPs To Catch Up With CNNs? [[paper](https://arxiv.org/abs/2105.15078)] 
- **[MixerGAN]** MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation [[paper](https://arxiv.org/abs/2105.14110)] 
- Less is More: Pay Less Attention in Vision Transformers [[paper](https://arxiv.org/abs/2105.14217)] 
- Can Attention Enable MLPs To Catch Up With CNNs? [[paper](https://arxiv.org/abs/2105.15078)]
- **[ResMLP]** ResMLP: Feedforward networks for image classification with data-efficient training [[paper](https://arxiv.org/abs/2105.03404)] 
- Pay Attention to MLPs [[paper](https://arxiv.org/abs/2105.08050)]
- Do You Even Need Attention? A Stack of Feed-Forward Layers Does SurprisinglyWell on ImageNet [[paper](https://arxiv.org/abs/2105.02723#:~:text=A%20Stack%20of%20Feed%2DForward%20Layers%20Does%20Surprisingly%20Well%20on%20ImageNet,-Luke%20Melas%2DKyriazi&text=The%20strong%20performance%20of%20vision,their%20multi%2Dhead%20attention%20layers.)]  [[code](https://github.com/lukemelas/do-you-even-need-attention?utm_source=catalyzex.com)] 
- **[RepMLP]** RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition [[paper](https://arxiv.org/abs/2105.01883)] [[code](https://github.com/DingXiaoH/RepMLP?utm_source=catalyzex.com)]
- Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks [[paper](https://arxiv.org/abs/2105.02358)] 
- **[MLP-Mixer]** MLP-Mixer: An all-MLP Architecture for Vision [[paper](https://arxiv.org/abs/2105.01601)]

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