  We introduce a generic framework that reduces the computational cost of
object detection while retaining accuracy for scenarios where objects with
varied sizes appear in high resolution images. Detection progresses in a
coarse-to-fine manner, first on a down-sampled version of the image and then on
a sequence of higher resolution regions identified as likely to improve the
detection accuracy. Built upon reinforcement learning, our approach consists of
a model (R-net) that uses coarse detection results to predict the potential
accuracy gain for analyzing a region at a higher resolution and another model
(Q-net) that sequentially selects regions to zoom in. Experiments on the
Caltech Pedestrians dataset show that our approach reduces the number of
processed pixels by over 50% without a drop in detection accuracy. The merits
of our approach become more significant on a high resolution test set collected
from YFCC100M dataset, where our approach maintains high detection performance
while reducing the number of processed pixels by about 70% and the detection
time by over 50%.

Computer Vision and Pattern Recognition