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whenxuan: update the readme
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‎README.md‎

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@@ -32,26 +32,14 @@ We only develop and test with PyTorch. Please make sure to install it from [PyTo
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## Modules <a id="Modules"></a>
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#### [`SEAttention`](https://github.com/wwhenxuan/Channel-Attention/blob/main/channel_attention/squeeze_excitation.py): [[paper]]() The Squeeze-and-Excitation Attention.
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<img width="80%" align="middle" src="images/SEAttention.png">
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#### 1. [`SEAttention`](https://github.com/wwhenxuan/Channel-Attention/blob/main/channel_attention/squeeze_excitation.py): [[paper]]() The Squeeze-and-Excitation Attention with Global Average Pooling and Feed Forward Network.
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<div style="text-align: center;">
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<img width="80%" src="images/SEAttention.png">
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</div>
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| Attention | Module Implementation Architecture |
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| ------------------------------------------------------------ | ------------------------------------------- |
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| <div style="text-align: center;"> SEAttention<br/>[[paper]]()</div> | ![SEAttention](images/SEAttention.png) |
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| <div style="text-align: center;"> ChannelAttention<br/>[[paper]]()</div> | ![SEAttention](images/ChannelAttention.png) |
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| <div style="text-align: center;"> SpatialAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> ConvBlockAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> EfficientChannelAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"><br />LocalAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> ConvBlockAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> ConvBlockAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> ConvBlockAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> ConvBlockAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> ConvBlockAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> ConvBlockAttention<br/>[[paper]]()</div> | |
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| <div style="text-align: center;"> ConvBlockAttention<br/>[[paper]]()</div> | |
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#### 2. [`ChannelAttention`](https://github.com/wwhenxuan/Channel-Attention/blob/main/channel_attention/channel_attention.py): [[paper]]() The Channel Attention with Global Average Pooling and Global Max Pooling.
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<div style="text-align: center;">
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<img width="80%" src="images/ChannelAttention.png">
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</div>
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#### 3. [`ECAAttention`]()

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