Efficient Object Detection in Large Images using Deep Reinforcement Learning

Reinforcement Learning for Efficient Detection Reinforcement Learning (RL) has been recently used to (1) replace classical detectors such as SSD and Faster-RCNN, (2) replace exhaustive box proposal techniques in two-stage detectors, and (3) find ROIs in very large images to run a detector on. Most of the methods proposed in this categories focus on learning sequential policies. Under category (1), [3, 29] proposed a top-down sequential object detection models trained with Q-learning algorithm. Most of the RL methods associated with object detection fall into category (2). For example, [16] recursively divides up an image in a top-down approach where the divisions are decided by the RL agent. The box proposals returned by the agent are then passed through Fast-RCNN.