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Deep Category-Level and Regularized Hashing With Global Semantic Similarity Learning 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 卷号: 51, 期号: 12, 页码: 6240-6252
作者:  Chen, Yaxiong;  Lu, Xiaoqiang
Adobe PDF(2358Kb)  |  收藏  |  浏览/下载：178/0  |  提交时间：2022/01/26
Semantics  Binary codes  Image retrieval  Force  Machine learning  Cybernetics  Benchmark testing  Category-level semantics  deep feature similarity  deep hashing  image retrieval  
Bio-Inspired Representation Learning for Visual Attention Prediction 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2021, 卷号: 51, 期号: 7, 页码: 3562-3575
作者:  Yuan, Yuan;  Ning, Hailong;  Lu, Xiaoqiang
Adobe PDF(2877Kb)  |  收藏  |  浏览/下载：182/0  |  提交时间：2021/07/12
Bio-inspired  center-bias prior  contrast features  densely connected  reduction-attention  semantic features  visual attention prediction (VAP)  
TTH-RNN: Tensor-Train Hierarchical Recurrent Neural Network for Video Summarization 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2021, 卷号: 68, 期号: 4, 页码: 3629-3637
作者:  Zhao, Bin;  Li, Xuelong;  Lu, Xiaoqiang
Adobe PDF(1796Kb)  |  收藏  |  浏览/下载：245/0  |  提交时间：2021/01/07
Recurrent neural networks  Training  Task analysis  Tensors  Matrix decomposition  Standards  Feature extraction  Hierarchical structure  tensor-train embedding layer  video summarization  
Person Reidentification via Unsupervised Cross-View Metric Learning 期刊论文
IEEE Transactions on Cybernetics, 2021, 卷号: 51, 期号: 4, 页码: 1849-1859
作者:  Feng, Yachuang;  Yuan, Yuan;  Lu, Xiaoqiang
Adobe PDF(1240Kb)  |  收藏  |  浏览/下载：177/1  |  提交时间：2021/04/13
Metric learning  person reidentification (Re-ID)  unsupervised learning  view-specific mapping  
Hyperspectral image super-resolution with self-supervised spectral-spatial residual network 期刊论文
Remote Sensing, 2021, 卷号: 13, 期号: 7
作者:  Chen, Wenjing;  Zheng, Xiangtao;  Lu, Xiaoqiang
Adobe PDF(2974Kb)  |  收藏  |  浏览/下载：200/0  |  提交时间：2021/04/21
hyperspectral image super-resolution  data fusion  spectral-spatial residual network  multispectral image  self-supervised training  
Spectral-Spatial Joint Sparse NMF for Hyperspectral Unmixing 期刊论文
IEEE Transactions on Geoscience and Remote Sensing, 2021, 卷号: 59, 期号: 3, 页码: 2391-2402
作者:  Dong, Le;  Yuan, Yuan;  Lu, Xiaoqiang
Adobe PDF(2358Kb)  |  收藏  |  浏览/下载：202/1  |  提交时间：2021/03/29
Global spatial structure group  local spectral group  nonnegative matrix factorization (NMF)  sparse expression  
SpectralSpatial Joint Sparse NMF for Hyperspectral Unmixing 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 卷号: 59, 期号: 3, 页码: 2391-2402
作者:  Dong, Le;  Yuan, Yuan;  Lu, Xiaoqiang
Adobe PDF(2358Kb)  |  收藏  |  浏览/下载：302/0  |  提交时间：2021/04/19
Global spatial structure group  local spectral group  nonnegative matrix factorization (NMF)  sparse expression  
Fine-Grained Visual Categorization by Localizing Object Parts With Single Image 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2021, 卷号: 23, 页码: 1187-1199
作者:  Zheng, Xiangtao;  Qi, Lei;  Ren, Yutao;  Lu, Xiaoqiang
Adobe PDF(3601Kb)  |  收藏  |  浏览/下载：200/0  |  提交时间：2021/05/28
Feature extraction  Detectors  Training  Image representation  Visualization  Semantics  Birds  Fine-grained visual categorization  Part localization  Part relationship  Spectral clustering  Dropout learning  
A Supervised Segmentation Network for Hyperspectral Image Classification 期刊论文
IEEE Transactions on Image Processing, 2021, 卷号: 30, 页码: 2810-2825
作者:  Sun, Hao;  Zheng, Xiangtao;  Lu, Xiaoqiang
Adobe PDF(4384Kb)  |  收藏  |  浏览/下载：237/1  |  提交时间：2021/03/24
Hyperspectral imaging  Feature extraction  Training  Task analysis  Imaging  Image segmentation  Testing  Hyperspectral image (HSI) classification  fully convolutional segmentation network (FCSN)  generalization  
Multisource Remote Sensing Data Classification With Graph Fusion Network 期刊论文
IEEE Transactions on Geoscience and Remote Sensing, 2021
作者:  Du, Xingqian;  Zheng, Xiangtao;  Lu, Xiaoqiang;  Doudkin, Alexander A.
Adobe PDF(3121Kb)  |  收藏  |  浏览/下载：259/0  |  提交时间：2021/02/08
Classification  deep learning  hyperspectral image (HSI)  light detection and ranging (LiDAR)  remote sensing