Bidet: An efficient binarized object detector

作     者：Wang, Ziwei Wu, Ziyi Lu, Jiwen Zhou, Jie 

作者机构：Department of Automation Tsinghua University China State Key Lab of Intelligent Technologies and Systems China Beijing National Research Center for Information Science and Technology China Tsinghua Shenzhen International Graduate School Tsinghua University China 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2020年

核心收录：

主　　题：Object recognition 

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 twostage 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-theart binary neural networks by a sizable margin. Copyright © 2020, The Authors. All rights reserved.