I am reading a recent paper which you can find here. It reintroduces a concept of Multiple Instance Learning for weak supervised object detection. I am quoting : Often WSOL is conceptualised as Multiple Instance Learning (MIL)  . Images are treated as bags of windows  (instances). A negative image contains only negative instances. A  positive image contains at least one positive instance, mixed in with  a majority of negative ones. The goal is to find the true positive  instances from which to learn a window classifier for the object  class. This typically happens by iteratively alternating between (A)  re-training the object detector given the current selec- tion of  positive instances and (B) re-localising instances in the positive  images using the current object detector.I am not able to understand (B). From what I have gathered, A is training an object detector that will propose an objectness score of a sub section of the input image. What is (B) doing ? What is being re localised ? 
What is Multiple Instance Learning for object detection?
deep learning;semi supervised learning;multitask learning