  We present a method for performing hierarchical object detection in images
guided by a deep reinforcement learning agent. The key idea is to focus on
those parts of the image that contain richer information and zoom on them. We
train an intelligent agent that, given an image window, is capable of deciding
where to focus the attention among five different predefined region candidates
(smaller windows). This procedure is iterated providing a hierarchical image
analysis.We compare two different candidate proposal strategies to guide the
object search: with and without overlap. Moreover, our work compares two
different strategies to extract features from a convolutional neural network
for each region proposal: a first one that computes new feature maps for each
region proposal, and a second one that computes the feature maps for the whole
image to later generate crops for each region proposal. Experiments indicate
better results for the overlapping candidate proposal strategy and a loss of
performance for the cropped image features due to the loss of spatial
resolution. We argue that, while this loss seems unavoidable when working with
large amounts of object candidates, the much more reduced amount of region
proposals generated by our reinforcement learning agent allows considering to
extract features for each location without sharing convolutional computation
among regions.
