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Are we ready for a new paradigm shift? A survey on visual deep MLP
Ruiyang Liu1, Yinghui Li2, Linmi Tao1
1Department of Computer Science and Technology, BNRist, Tsinghua University & Key Lab of Pervasive Computing, Ministry of Education of China, Beijing 100084, China.
Patterns (New York, N.Y.)
|July 18, 2022
Summary
Deep multilayer perceptron (MLP) models show promise as a new computer vision paradigm. This review analyzes MLPs against convolution and self-attention, exploring their potential for future computing devices.
Area of Science:
- Computer Vision
- Deep Learning
- Artificial Intelligence
Background:
- Deep multilayer perceptron (MLP) models are gaining traction in the vision community.
- Historical paradigm shifts in computer vision were driven by large datasets and increased computing power.
Purpose of the Study:
- To evaluate if MLPs can establish a new paradigm in computer vision.
- To compare MLPs with existing dominant methods like convolution and self-attention.
Main Methods:
- Detailed comparison of intrinsic connections and differences between convolution, self-attention, and token-mixing MLPs.
- Analysis of token-mixing MLP advantages and limitations.
- Review of recent MLP-like variants, module designs, network architectures, and applications.
Main Results:
- MLPs, convolution, and self-attention represent current mainstream approaches for weighted summations in the GPU era.
- Token-mixing MLPs offer specific advantages and limitations that warrant detailed examination.
Conclusions:
- MLPs present a potential new paradigm for computer vision.
- Future development of MLPs should consider integration with next-generation computing devices.
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