Paper mentioned in the question title deals with localization of certain objects in images. Paper mentions generating multiple types of object masks using deep neural network based on ImageNet, proposing multiple bounding boxes and scoring such proposals. However I can not find how the bounding box proposals are generated. If paper does not mention this method, what kind of algorithm would you suggest to generate such proposals?
Solved – How are bounding box proposals generated in Google’s paper “Deep Neural Networks for Object Detection”
computer visiondeep learningobject detection
Best Answer
Section "4 Detection as DNN Regression" explains that they used a neural network to obtain the bounding boxes: