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AttentionNet: Aggregating Weak Directions for Accurate Object Detection

Donggeun Yoo et al. — ICCV (2015)

We present a novel detection method using a deep convolutional neural network (CNN), named AttentionNet. We cast an object detection problem as an iterative classification problem, which is the most suitable form of a CNN. AttentionNet provides quantized weak directions pointing a target object and the ensemble of iterative predictions from AttentionNet converges to an accurate object boundary box. Since AttentionNet is a unified network for object detection, it detects objects without any separated models from the object proposal to the post bounding-box regression. We evaluate AttentionNet by a human detection task and achieve the state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 with an 8-layered architecture only.

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Donggeun Yoo1, Sunggyun Park1, Joo-Young Lee2, Anthony Paek1 and In So Kweon2

1Lunit Inc., 2KAIST

ICCV (2015)

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