Image Models

ConvMLP

Introduced by Li et al. in ConvMLP: Hierarchical Convolutional MLPs for Vision

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 Vision

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Image Classification 1 25.00%
Instance Segmentation 1 25.00%
Object Detection 1 25.00%
Semantic Segmentation 1 25.00%

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