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We present a novel efficient object detection and localization frameworkbased on the probabilistic bisection algorithm. A Convolutional Neural Network(CNN) is trained and used as a noisy oracle that provides answers to inputquery images. The responses along with error probability estimates obtainedfrom the CNN are used to update beliefs on the object location along eachdimension. We show that querying along each dimension achieves the same lowerbound on localization error as the joint query design. Finally, we compare ourapproach to the traditional sliding window technique on a real world facelocalization task and show speed improvements by at least an order of magnitudewhile maintaining accurate localization.

Active query-driven visual search using probabilistic bisection and convolutional neural networks