From the introduction, so far, it must be clear to you that the attention mechanism works on a sequence of objects, assigning each element in the sequence a weight for a specific iteration of a required output. With every next step, not only the sequence but also the weights in the attention mechanism can change. So, attention-based architectures are essentially sequence networks, best implemented in deep learning using RNNs (or their variants).
The question now is: how do we implement a sequence-based attention on a static image, especially the one represented in a convolutional neural network (CNN)? Well, let's take an example that sits right in between a text and image to understand this. Assume ...
