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We present a novel detection method using a deep convolutional neural network(CNN), named AttentionNet. We cast an object detection problem as an iterativeclassification problem, which is the most suitable form of a CNN. AttentionNetprovides quantized weak directions pointing a target object and the ensemble ofiterative predictions from AttentionNet converges to an accurate objectboundary box. Since AttentionNet is a unified network for object detection, itdetects objects without any separated models from the object proposal to thepost bounding-box regression. We evaluate AttentionNet by a human detectiontask and achieve the state-of-the-art performance of 65% (AP) on PASCAL VOC2007/2012 with an 8-layered architecture only.

AttentionNet: Aggregating Weak Directions for Accurate Object Detection