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Artificial Intelligence (AI) Mastering Development

How is visual attention mechanism different from a two branch convolutional neural network?

I am doing some research on the visual attention mechanism in remote sensing domain (where the features learnt from one layer are highlighted using the attention mask derived from another layer). From what I have observed, the attention mask is learnt in a similar fashion as any other branch in CNN. So, what is so special about the visual attention mask that makes it different from a regular two branch CNN? The reference papers are provided below:

A paper on visual attention

A paper on two branch CNN

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