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.

本研究提出了一种名为 BiDet 的二值化神经网络学习方法，通过信息瓶颈原理的泛化，学习稀疏的物体先验，以消除信息冗余并改善误检测，从而实现高精度的物体检测。