In this section, we will try to understand how deep reinforcement learning can be applied for hierarchical object detection as per the framework suggested in Hierarchical Object Detection with Deep Reinforcement Learning by Bellver et. al. (2016)(https://arxiv.org/pdf/1611.03718.pdf). This experiment showcases a method to perform hierarchical object detection in images using deep reinforcement learning with the main focus on important parts of the image carrying richer information. The objective here was to train a deep reinforcement learning agent to which an image window is given and the image gets further segregated into five smaller windows and the agent is successfully able to focus its attention on one of the smaller windows.
Now let's consider how we humans look at an image. We always extract information in a sequential manner to understand the content of the image:
- First, we focus on the most important part of the image...
