Is there a good way to understand how single-shot object detection works? The most basic way to do detection is use a sliding-window detector and look at the output of the NN to detect if a class is there or not.
I'm wondering if there is a way to understand how many of the single-shot detectors work? Internally is there some form of sliding window going on? Or is it basically the same detector learned at each point?