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Improving Generalization of Deepfake Detectors by Imposing Gradient Regularization 期刊论文
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2024, 卷号: 19, 期号: 2024, 页码: 5345-5356
作者:  Weinan Guan;  Wei Wang;  Jing Dong;  Bo Peng
Adobe PDF(1989Kb)  |  收藏  |  浏览/下载：33/9  |  提交时间：2024/06/21
Deepfake detection  forgery texture patterns  
Pavement Defect Detection with Deep Learning: A Comprehensive Survey 期刊论文
IEEE Transactions on Intelligent Vehicles, 2023, 卷号: 9, 期号: 3, 页码: 4292 - 4311
作者:  Lili Fan;  Dandan Wang;  Junhao Wang;  Yunjie Li;  Yifeng Cao;  Yi Liu;  Xiaoming Chen;  Yutong Wang
Adobe PDF(6287Kb)  |  收藏  |  浏览/下载：40/10  |  提交时间：2024/06/06
Deep learning  pavement defect detection  computer vision  image processing  3D image  
Two-particle Debris Flow Simulation Based on SPH 期刊论文
Computer Animation and Virtual Worlds, 2024, 卷号: 35, 期号: 3, 页码: 1-17
作者:  Zhang JX(张佳岫);  Yang M(杨猛);  Xiaomin Li;  Qunou Jiang;  Heng Zhang;  Meng WL(孟维亮)
Adobe PDF(4962Kb)  |  收藏  |  浏览/下载：28/11  |  提交时间：2024/06/04
debris flow  GPU acceleration  natural disaster simulation  SPH  
Toward a Fast and Robust MI-BCI: Online Adaptation of Stimulus Paradigm and Classification Model 期刊论文
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 卷号: 73, 页码: 12
作者:  Wang, Jiaxing;  Wang, Weiqun;  Su, Jianqiang;  Wang, Yihan;  Hou, Zeng-Guang
Adobe PDF(1810Kb)  |  收藏  |  浏览/下载：64/7  |  提交时间：2024/05/30
Brain-computer interface (BCI)  classification model adaption  information transfer rate (ITR)  motor imagery (MI) duration  stimulus paradigm adjustment  
A Multi-Modal Classification Method for Early Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease Using Three Paradigms With Various Task Difficulties 期刊论文
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024, 卷号: 32, 页码: 1456-1465
作者:  Chen Sheng;  Zhang Chutian;  Yang Hongjun;  Peng Liang;  Xie Haiqun;  Lv Zeping;  Hou Zeng-Guang
Adobe PDF(10190Kb)  |  收藏  |  浏览/下载：38/10  |  提交时间：2024/05/29
Dementia  multi-modal  machine learning  domain-adversarial neural network  
Enhancing ENSO predictions with self-attention ConvLSTM and temporal embeddings 期刊论文
Frontiers in Marine Science, 2024, 卷号: 11, 页码: 1334210
作者:  Rui, Chuang;  Sun, Zhengya;  Zhang, Wensheng;  Liu, Anan;  Wei, Zhiqiang
Adobe PDF(14474Kb)  |  收藏  |  浏览/下载：29/9  |  提交时间：2024/05/28
El Niño-Southern Oscillation (ENSO)  deep learning for ENSO prediction  self-attention ConvLSTM  temporal embeddings  spring prediction barrier  
Covariant Peak Constraint for Accurate Keypoint Detection and Keypoint-Specific Descriptor Learning 期刊论文
IEEE Transactions on Multimedia, 2024, 卷号: 26, 页码: 5383 - 5397
作者:  Fu Yujie;  Zhang Pengju;  Tang Fulin;  Wu Yihong
Adobe PDF(15150Kb)  |  收藏  |  浏览/下载：40/9  |  提交时间：2024/05/28
Image Matching  Local Feature Extraction  Covariant Peak Constraint  Conditional Neural Reprojection Error  
Bridging the gap with grad: Integrating active learning into semi-supervised domain generalization 期刊论文
NEURAL NETWORKS, 2024, 卷号: 171, 页码: 186-199
作者:  Li, Jingwei;  Li, Yuan;  Tan, Jie;  Liu, Chengbao
Adobe PDF(2408Kb)  |  收藏  |  浏览/下载：76/8  |  提交时间：2024/03/26
Domain generalization  Semi-supervised learning  Active learning  
Computational Experiments for Complex Social Systems: Experiment Design and Generative Explanation 期刊论文
IEEE/CAA Journal of Automatica Sinica, 2024, 卷号: 11, 期号: 4, 页码: 1022-1038
作者:  Xiao Xue;  Deyu Zhou;  Xiangning Yu;  Gang Wang;  Juanjuan Li;  Xia Xie;  Lizhen Cui;  Fei-Yue Wang
Adobe PDF(7239Kb)  |  收藏  |  浏览/下载：64/15  |  提交时间：2024/03/18
Agent-based modeling  computational experiments  cyber-physical-social systems (CPSS)  generative deduction  generative experiments  meta model  
Bionic Underwater Vehicle: A Data-Driven Disturbance Rejection Control Framework 期刊论文
IEEE ROBOTICS & AUTOMATION MAGAZINE, 2023, 卷号: 31, 期号: 1, 页码: 18-28
作者:  Wang, Kaihui;  Zou, Wei;  Ma, Ruichen;  Lv, Jiaqi;  Su, Hu;  Wang, Yu;  Ma, Hongxuan
Adobe PDF(2970Kb)  |  收藏  |  浏览/下载：75/8  |  提交时间：2024/02/22
Vehicle dynamics  Robots  Propulsion  Predictive models  Biological system modeling  Robustness  Disturbance observers