As we discovered in the NLP example covered in the earlier section on Attention Mechanism - Intuition, Attention did help us a lot in both achieving new use-cases, not optimally feasible with conventional NLP, and vastly improving the performance of the existing NLP mechanism. Similar is the usage of Attention in CNN and Visual Models as well
In the earlier chapter Chapter 7, Object-Detection & Instance-Segmentation with CNN, we discovered how Attention (like) mechanism are used as Region Proposal Networks for networks like Faster R-CNN and Mask R-CNN, to greatly enhance and optimize the proposed regions, and enable the generation of segment masks. This corresponds to the first part of the discussion. In this section, we will cover the second part of the discussion, where we will use 'Attention' mechanism to improve the performance of our CNNs, even under extreme conditions.
