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Apple detection during different growth stages in orchards using the improved YOLOV3 model 期刊论文
Computers and Electronics in Agriculture, 2019, 期号: 157, 页码: 417-426
作者:  Tian YN(田雨农)
Adobe PDF(4028Kb)  |  收藏  |  浏览/下载:131/42  |  提交时间:2022/01/07
Apple images acquisition  Image augmentation  Deep learning  YOLOV3-dense  Real-time detection  
Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense 期刊论文
Journal of Sensors, 2019, 期号: 2019, 页码: 1-13
作者:  Tian YN(田雨农)
Adobe PDF(26755Kb)  |  收藏  |  浏览/下载:143/34  |  提交时间:2022/01/06
optical sensors  deep learning  lesion detection  CycleGAN  DenseNet  YOLO-V3 model  
Event Co-reference Resolution via a Multi-loss Neural Network without Using Argument Information 期刊论文
Science China Information Sciences, 2019, 卷号: 62, 期号: 11, 页码: 212101:1–212101:9
作者:  Xinyu Zuo;  Yubo Chen;  Kang Liu;  Jun Zhao
Adobe PDF(360Kb)  |  收藏  |  浏览/下载:157/51  |  提交时间:2021/06/18
Event co-reference resolution  Neural network  Information extraction  Multi-loss function  Event extraction  
Large-scale Data Collection and Analysis via a Gamified Intelligent Crowdsourcing Platform 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 427-436
作者:  Simone Hantke;  Tobias Olenyi;  Christoph Hausner;  Tobias Appel;  Björn Schuller
浏览  |  Adobe PDF(1345Kb)  |  收藏  |  浏览/下载:104/37  |  提交时间:2021/02/22
Human computation  speech analysis  crowdsourcing  gamified data collection  survey.  
Content-Based Visual Summarization for Image Collections 期刊论文
IEEE Transactions on Visualization and Computer Graphics, 2019, 期号: 0, 页码: 1-1
作者:  Pan, Xingjia;  Tang, Fan;  Dong, Weiming;  Ma, Chongyang;  Meng, Yiping;  Huang, Feiyue;  Lee, Tong-yee;  Xu, Changsheng
浏览  |  Adobe PDF(26249Kb)  |  收藏  |  浏览/下载:260/44  |  提交时间:2020/12/18
Collage  layout  hidden-topic  tree-based  content-aware  summeration  
Attention-Based Pedestrian Attribute Analysis 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 12, 页码: 6126-6140
作者:  Zichang Tan;  Yang Yang;  Jun Wan;  Hanyuan Hang;  Guodong Guo;  Stan Z. Li
Adobe PDF(3457Kb)  |  收藏  |  浏览/下载:205/37  |  提交时间:2020/10/27
Pedestrian attribute analysis  attention mechanism  pedestrian parsing  
Efficient Face Alignment with Fast Normalization and Contour Fitting Loss 期刊论文
ACM Transactions on Multimedia Computing, Communications, and Applications, 2019, 期号: 3, 页码: 16
作者:  Liu, Zhiwei;  Zhu, Xiangyu;  Tang, Ming;  Lei, Zhen;  Wang, Jinqiao
浏览  |  Adobe PDF(1359Kb)  |  收藏  |  浏览/下载:218/53  |  提交时间:2020/09/10
Face alignment, convolutional neural networks, real-time, semantic meaning  
Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data 期刊论文
NeuroImage: Clinical, 2019, 卷号: 26, 期号: 102080, 页码: 1-9
作者:  Sun, Hailun;  Jiang, Rongtao;  Qi, Shile;  Katherine, L., Narr;  Benjamin, SC, Wade;  Joel, Upston;  Randall, Espinoza;  Tom, Jones;  Vince, D., Calhoun;  Christopher, C, Abbott;  Sui, Jing
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Individualized prediction  Electroconvulsive therapy (ECT)  Functional connectivity (FC)  Major depressive disorder (DEP)  Resting-state fMRI  HDRS  Treatment response  
Bioinformatics Methodologies to Identify Interactions Between Type 2 Diabetes and Neurological Comorbidities 期刊论文
IEEE ACCESS, 2019, 期号: 7, 页码: 183948-183970
作者:  Rahman, Md Habibur;  Peng, Silong;  Hu, Xiyuan;  Chen, Chen;  Uddin, Shahadat;  Quinn, Julian M. W.;  Moni, Mohammad Ali
浏览  |  Adobe PDF(9657Kb)  |  收藏  |  浏览/下载:307/41  |  提交时间:2020/03/30
Bioinformatics  comorbidities  gene set enrichment analysis  gene ontology  neurological disease  pathway  semantic similarity  Type 2 diabetes  
Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units 期刊论文
IEEE ACCESS, 2019, 卷号: 7, 页码: 150743-150756
作者:  Zhang, Yong;  Fan, Yanbo;  Dong, Weiming;  Hu, Bao-Gang;  Ji, Qiang
收藏  |  浏览/下载:267/0  |  提交时间:2020/03/30
Gold  Estimation  Hidden Markov models  Training  Face  Task analysis  Neural networks  Facial action units  intensity estimation  deep learning  weakly supervised learning