BiDet：一种高效的二值化目标检测器
BiDet: An Efficient Binarized Object Detector
摘要：
在本文中，我们提出了一个二值化的神经网络学习方法称为BiDet有效的目标检测。传统的网络二值化方法直接对表示能力受限的一级或两级检测器的权值和激活值进行二值化，使得网络中的信息冗余导致大量的误报，使网络性能显著下降。相反，我们的BiDet充分利用二进制神经网络的表示能力，通过冗余去除目标检测，通过减少误报，提高检测精度。具体来说，我们推广的信息瓶颈（IB）原则的对象检测，在高层次的特征图的信息量受到限制和互信息之间的特征图和目标检测最大化。同时，我们学习稀疏的对象先验，使后验集中在信息检测预测与假阳性消除。在PASCAL VOC和COCO数据集上的大量实验表明，我们的方法比最先进的二进制神经网络有相当大的优势。
英文摘要：
In this paper, we propose a binarized neural network learning method called BiDet for efficient object detection. Conventional network binarization methods directly quantize the weights and activations in one-stage or two-stage detectors with constrained representational capacity, so that the information redundancy in the networks causes numerous false positives and degrades the performance significantly. On the contrary, our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal, through which the detection precision is enhanced with alleviated false positives. Specifically, we generalize the information bottleneck (IB) principle to object detection, where the amount of information in the high-level feature maps is constrained and the mutual information between the feature maps and object detection is maximized. Meanwhile, we learn sparse object priors so that the posteriors are concentrated on informative detection prediction with false positive elimination. Extensive experiments on the PASCAL VOC and COCO datasets show that our method outperforms the state-of-the-art binary neural networks by a sizable margin.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
