Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition
文献类型：期刊论文
| 作者 | Ma, Chao3; An, Wei3; Lei, Yinjie1; Guo, Yulan2,3 |
| 刊名 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
 |
| 出版日期 | 2019 |
| 卷号 | 68期号:1页码:38-48 |
| 关键词 | 3-D object recognition convolutional neural network (CNN) deep learning (DL) network binarization volumetric representation |
| ISSN号 | 0018-9456 |
| DOI | 10.1109/TIM.2018.2840598 |
| 英文摘要 | To address the high computational and memory cost in 3-D volumetric convolutional neural networks (CNNs), we propose an approach to train binary volumetric CNNs for 3-D object recognition. Our method is specifically designed for 3-D data, in which it transforms the inputs and weights in convolutional/fully connected layers to binary values, which can potentially accelerate the networks by efficient bitwise operations. Two loss calculation methods are designed to solve the accuracy decrease problem when the weights in the last layer are binarized. Four binary volumetric CNNs are obtained from their corresponding floating-point networks using our approach. Evaluations on three public datasets from different domains (Computer Aided Design (CAD), light detection and ranging (LiDAR), and RGB-D) show that our binary volumetric CNNs can achieve a comparable recognition performance as their floating-point counterparts but consume less computational and memory resources. |
| 资助项目 | National Natural Science Foundation of China[61403265] ; National Natural Science Foundation of China[61602499] ; National Natural Science Foundation of China[61471371] ; Science and Technology Plan of Sichuan Province[2015SZ0226] ; National Postdoctoral Program for Innovative Talents[BX201600172] ; China Postdoctoral Science Foundation |
| WOS研究方向 | Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000452611600003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/3514]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Guo, Yulan |
| 作者单位 | 1.Sichuan Univ, Coll Elect & Informat Engn, Chengdu 610065, Sichuan, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Natl Univ Def Technol, Coll Elect Sci, Changsha 410073, Hunan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Chao,An, Wei,Lei, Yinjie,et al. Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2019,68(1):38-48. |
| APA | Ma, Chao,An, Wei,Lei, Yinjie,&Guo, Yulan.(2019).Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,68(1),38-48. |
| MLA | Ma, Chao,et al."Binary Volumetric Convolutional Neural Networks for 3-D Object Recognition".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 68.1(2019):38-48. |
入库方式： OAI收割
来源：计算技术研究所
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