Enhanced object detection via fusion with prior beleefs from image classification
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Enhanced object detection via fusion with prior beleefs from image classification
通过融合图像分类与先验 beleefs来提高目标检测性能
Yilun Cao∗y, Hyungtae Lee∗zx, and Heesung Kwonx
yUniversity of Southern California, Los Angeles, California, U.S.A.
zBooz Allen Hamilton Inc., McLean, Virginia, U.S.A.
xU.S. Army Research Laboratory, Adelphi, Maryland, U.S.A.
yiluncao@usc.edu, lee hyungtae@bah.com, heesung.kwon.civ@mail.mil
ABSTRACT
文中引入一种新的融合方法以提高目标检测性能，融合两不同类型的计算机视觉任务进行决策：对象检测和图像分类。在提议的工作中，从图像分类得到的类标签被视为某些似有似无对象的先验知识。先验知识进行物体检测的决策融合，通过物体检测器与先验知识的矛盾减少误报的，提高检测精度。最近推出的新的融合方法称为动态意见融合（DBF），用于融合检测器输出和分类先验。实验结果表明，通过提供的融合框架，过去工作中使用的基准数据集上的所有检测算法的检测性能均有所提高。
Index Terms— dynamic belief fusion, object detection, image classification
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