AttentionNet: Aggregating Weak Directions for Accurate Object Detection

Donggeun Yoo, Sunggyun Park, Joon-Young Lee, Anthony S. Paek, In So Kweon; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 2659-2667

Abstract


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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[bibtex]
@InProceedings{Yoo_2015_ICCV,
author = {Yoo, Donggeun and Park, Sunggyun and Lee, Joon-Young and Paek, Anthony S. and So Kweon, In},
title = {AttentionNet: Aggregating Weak Directions for Accurate Object Detection},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}