ConvMLP is a hierarchical convolutional MLP for visual recognition, which consists of a stage-wise, co-design of convolution layers, and MLPs. The Conv Stage consists of $C$ convolutional blocks with $1\times 1$ and $3\times 3$ kernel sizes. It is repeated $M$ times before a down convolution is utilized to express a level $L$. The MLP-Conv Stage consists of Channelwise MLPs, with skip layers, and a depthwise convolution. This is repeated $M$ times before a down convolution is utilized to express a level $\mathcal{L}$.
Source: ConvMLP: Hierarchical Convolutional MLPs for VisionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Image Classification | 1 | 25.00% |
Instance Segmentation | 1 | 25.00% |
Object Detection | 1 | 25.00% |
Semantic Segmentation | 1 | 25.00% |
Component | Type |
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Convolution
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Convolutions | |
Dense Connections
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Feedforward Networks | |
Depthwise Convolution
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Convolutions | |
Residual Connection
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Skip Connections |