Chapter 9 - Image Segmentation

1. How does up-scaling help in U-Net architecture?
   Upscaling helps the feature map to increase in size so that the final output is the same size as the input size.
2. Why do we need to have a fully convolutional network in U-Net?
   Because the outputs are also images, and it is difficult to predict an image shaped tensor using the Linear layer.
3. How does RoI Align improve over RoI pooling in Mask R-CNN?
   RoI Align takes offsets of predicted proposals to fine-align the feature map.
4. What is the major difference between U-Net and Mask R-CNN for segmentation?
   U-Net is fully convolutional and with a single end2end network, whereas Mask R-CNN uses mini networks such as Backbone, RPN, etc to do different tasks. Mask R-CNN is capable of identifying and separating several objects of the same type, but U-Net can only identify (but not separate them into individual instances).
5. What is instance segmentation?
   If there are different objects of the same class in the same image...