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Knowledge-driven Egocentric Multimodal Activity Recognition 期刊论文
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2020, 卷号: 16, 期号: 4, 页码: 21
作者:  Huang, Yi;  Yang, Xiaoshan;  Gao, Junyu;  Sang, Jitao;  Xu, Changsheng
Adobe PDF(1875Kb)  |  收藏  |  浏览/下载：415/64  |  提交时间：2021/03/08
Egocentric videos  wearable sensors  graph neural networks  
Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis 期刊论文
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 卷号: 28, 期号: 12, 页码: 3204-3218
作者:  Shen, Tianyu;  Wang, Jiangong;  Gou, Chao;  Wang, Fei-Yue
Adobe PDF(3530Kb)  |  收藏  |  浏览/下载：327/72  |  提交时间：2021/03/02
Image segmentation  Biomedical imaging  Fuzzy sets  Breast cancer  Breast cancer  deep learning (DL)  fuzzy classifier (FC)  interval type-2 possibilistic fuzzy c-means (IT2PFCM)  
Deep prototypical networks based domain adaptation for fault diagnosis 期刊论文
JOURNAL OF INTELLIGENT MANUFACTURING, 2020, 页码: 11
作者:  Wang, Huanjie;  Bai, Xiwei;  Tan, Jie;  Yang, Jiechao
Adobe PDF(1084Kb)  |  收藏  |  浏览/下载：263/58  |  提交时间：2021/01/06
Bearing  Fault diagnosis  Domain adaptation  Prototype learning  
Domain Adaptation by Class Centroid Matching and Local Manifold Self-Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 9703-9718
作者:  Tian, Lei;  Tang, Yongqiang;  Hu, Liangchen;  Ren, Zhida;  Zhang, Wensheng
Adobe PDF(3443Kb)  |  收藏  |  浏览/下载：363/73  |  提交时间：2021/01/06
Domain adaptation  class centroid matching  local manifold self-learning  
CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection 期刊论文
Pattern Recognition, 2020, 卷号: 109, 期号: 0, 页码: 10
作者:  Zou W(邹伟)
浏览  |  Adobe PDF(1839Kb)  |  收藏  |  浏览/下载：172/50  |  提交时间：2020/10/22
Weakly supervised learning, Automated surface inspection, Defect detection, Knowledge distillation  
Local-Aggregation Graph Networks 期刊论文
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 卷号: 42, 期号: 11, 页码: 2874-2886
作者:  Jianlong Chang;  Lingfeng Wang;  Gaofeng Meng;  Shiming Xiang;  Chunhong Pan
浏览  |  Adobe PDF(3090Kb)  |  收藏  |  浏览/下载：271/96  |  提交时间：2020/10/20
Local-aggregation function  local-aggregation graph neural network  non-Euclidean structured signal  
Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition 期刊论文
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2020, 卷号: 18, 期号: 0, 页码: 1-5
作者:  Huang, Xiayuan;  Yang, Qiao;  Qiao, Hong
浏览  |  Adobe PDF(736Kb)  |  收藏  |  浏览/下载：190/72  |  提交时间：2020/10/13
Lightweight  synthetic aperture radar (SAR) target recognition  two-stream convolutional neural network (CNN)  
Multisource Transfer Learning for Cross-Subject EEG Emotion Recognition 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2020, 卷号: 50, 期号: 7, 页码: 3281-3293
作者:  Li, Jinpeng;  Qiu, Shuang;  Shen, Yuan-Yuan;  Liu, Cheng-Lin;  He, Huiguang
Adobe PDF(2274Kb)  |  收藏  |  浏览/下载：310/1  |  提交时间：2020/08/03
Brain modeling  Electroencephalography  Emotion recognition  Data models  Training  Calibration  Training data  Brain-computer interface  emotion recognition  transfer learning (TL)  
Domain Adaptation for EEG Emotion Recognition Based on Latent Representation Similarity 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2020, 卷号: 12, 期号: 2, 页码: 344-353
作者:  Li, Jinpeng;  Qiu, Shuang;  Du, Changde;  Wang, Yixin;  He, Huiguang
Adobe PDF(2274Kb)  |  收藏  |  浏览/下载：227/3  |  提交时间：2020/08/03
Electroencephalography  Brain modeling  Emotion recognition  Adaptation models  Training  Feature extraction  Neural networks  Domain adaptation  electroencephalogram (EEG)  emotion recognition  neural network  transfer learning  
Triplet Adversarial Domain Adaptation for Pixel-Level Classification of VHR Remote Sensing Images 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 卷号: 58, 期号: 5, 页码: 3558-3573
作者:  Yan, Liang;  Fan, Bin;  Liu, Hongmin;  Huo, Chunlei;  Xiang, Shiming;  Pan, Chunhong
Adobe PDF(6348Kb)  |  收藏  |  浏览/下载：378/76  |  提交时间：2020/06/22
Domain adaptation (DA)  pixel-level classification  self-training  triplet adversarial learning  very high resolution (VHR)