CASIA OpenIR
(本次检索基于用户作品认领结果)

浏览/检索结果: 共6条,第1-6条 帮助

限定条件            
已选(0)清除 条数/页:   排序方式:
Long/Short-Term Appearance Modeling and Two-Step Association for Multi-Object Tracking 会议论文
, Auckland, NZ, 2019.10.27-2019.10.30
作者:  Yating, Liu;  Xuesong, Li;  Kunfeng, Wang;  Yong, Yan;  Feiyue, Wang
Adobe PDF(1936Kb)  |  收藏  |  浏览/下载:177/44  |  提交时间:2022/06/16
网络系统实验平台:发展现状及展望 期刊论文
自动化学报, 2019, 卷号: 45, 期号: 9, 页码: 1637-1654
作者:  杨林瑶;  韩双双;  王晓;  李玉珂;  王飞跃
Adobe PDF(2557Kb)  |  收藏  |  浏览/下载:345/122  |  提交时间:2019/09/25
仿真软件  网络仿真  平行网络  计算实验  虚实交互  
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 8, 页码: 3049-3058
作者:  Chen, Yuanyuan;  Lv, Yisheng;  Wang, Xiao;  Li, Lingxi;  Wang, Fei-Yue
浏览  |  Adobe PDF(2273Kb)  |  收藏  |  浏览/下载:444/111  |  提交时间:2019/08/28
Deep learning  social transportation  traffic information detection  social media  text mining  
Cascade learning from adversarial synthetic images for accurate pupil detection 期刊论文
PATTERN RECOGNITION, 2019, 卷号: 88, 期号: 2020, 页码: 584-594
作者:  Gou, Chao;  Zhang, Hui;  Wang, Kunfeng;  Wang, Fei-Yue;  Ji, Qiang
浏览  |  Adobe PDF(2057Kb)  |  收藏  |  浏览/下载:433/122  |  提交时间:2019/07/12
Cascade regression  GANs  Pupil detection  
Parallel Vehicular Networks: A CPSS-Based Approach via Multimodal Big Data in IoV 期刊论文
IEEE INTERNET OF THINGS JOURNAL, 2019, 卷号: 6, 期号: 1, 页码: 1079-1089
作者:  Han, Shuangshuang;  Wang, Xiao;  Zhang, Jun Jason;  Cao, Dongpu;  Wang, Fei-Yue
浏览  |  Adobe PDF(1880Kb)  |  收藏  |  浏览/下载:435/75  |  提交时间:2019/07/12
Cyber-social-physical system (CPSS)  Internet of Vehicles (IoV)  parallel system  social networks  
Pattern Sensitive Prediction of Traffic Flow Based on Generative Adversarial Framework 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 6, 页码: 2395-2400
作者:  Lin, Yilun;  Dai, Xingyuan;  Li, Li;  Wang, Fei-Yue
浏览  |  Adobe PDF(623Kb)  |  收藏  |  浏览/下载:371/160  |  提交时间:2019/05/07
Traflic flow prediction  deep learning  generative adversarial network