  We present a novel efficient object detection and localization framework
based on the probabilistic bisection algorithm. A Convolutional Neural Network
(CNN) is trained and used as a noisy oracle that provides answers to input
query images. The responses along with error probability estimates obtained
from the CNN are used to update beliefs on the object location along each
dimension. We show that querying along each dimension achieves the same lower
bound on localization error as the joint query design. Finally, we compare our
approach to the traditional sliding window technique on a real world face
localization task and show speed improvements by at least an order of magnitude
while maintaining accurate localization.
