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A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT VEHICLES, 2022, 卷号: 7, 期号: 3, 页码: 441-455
作者:  Wang, Jiangong;  Shen, Tianyu;  Tian, Yonglin;  Wang, Yutong;  Gou, Chao;  Wang, Xiao;  Yao, Fei;  Sun, Changyin
Adobe PDF(2602Kb)  |  收藏  |  浏览/下载：280/65  |  提交时间：2022/11/28
Object detection  Feature extraction  Data models  Training  Knowledge engineering  Detectors  Computational modeling  Computer vision  Unsupervised Domain Adaptation  Teacher-student learning  Traffic object detection  
Joint image-to-image translation with denoising using enhanced generative adversarial networks 期刊论文
SIGNAL PROCESSING-IMAGE COMMUNICATION, 2021, 卷号: 91, 页码: 9
作者:  Yan, Lan;  Zheng, Wenbo;  Wang, Fei-Yue;  Gou, Chao
Adobe PDF(4437Kb)  |  收藏  |  浏览/下载：298/45  |  提交时间：2021/03/01
Image-to-image translation  Generative adversarial networks  Image enhancement  Image denoising  
A novel background subtraction algorithm based on parallel vision and Bayesian GANs 期刊论文
NEUROCOMPUTING, 2020, 卷号: 394, 页码: 178-200
作者:  Zheng, Wenbo;  Wang, Kunfeng;  Wang, Fei-Yue
收藏  |  浏览/下载：239/0  |  提交时间：2020/06/22
Background subtraction  Background model  Bayesian generative adversarial network  Convolutional neural networks  Parallel vision  
Progress and Outlook of Visual Tracking: Bibliographic Analysis and Perspective 期刊论文
IEEE ACCESS, 2019, 卷号: 7, 页码: 184581-184598
作者:  Liu, Yating;  Wang, Kunfeng;  Li, Xuesong;  Bai, Tianxiang;  Wang, Fei-Yue
Adobe PDF(4276Kb)  |  收藏  |  浏览/下载：205/33  |  提交时间：2020/03/30
Visual tracking  bibliographic analysis  collaboration patterns  research hotspots  parallel vision  
DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending 期刊论文
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 卷号: 103, 页码: 142-157
作者:  Dai, Xingyuan;  Fu, Rui;  Zhao, Enmin;  Zhang, Zuo;  Lin, Yilun;  Wang, Fei-Yue;  Li, Li
Adobe PDF(5109Kb)  |  收藏  |  浏览/下载：336/29  |  提交时间：2019/09/30
Traffic prediction  Deep learning  Detrending  Multi-scale traffic prediction