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CASIA OpenIR
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IDO: Instance dual-optimization for weakly supervised object detection 期刊论文
APPLIED INTELLIGENCE, 2023, 页码: 18
作者:  Ren, Zhida;  Tang, Yongqiang;  Zhang, Wensheng
Adobe PDF(3668Kb)  |  收藏  |  浏览/下载：45/1  |  提交时间：2023/11/17
Deep learning  Weakly supervised learning  Object detection  Multiple instance learning  
Class-Oriented Self-Learning Graph Embedding for Image Compact Representation 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 卷号: 33, 期号: 1, 页码: 74-87
作者:  Hu, Liangchen;  Dai, Zhenlei;  Tian, Lei;  Zhang, Wensheng
收藏  |  浏览/下载：177/0  |  提交时间：2023/03/20
Sparse matrices  Manifolds  Machine learning algorithms  Laplace equations  Heuristic algorithms  Data models  Data mining  Adaptive graph learning  separability examination  marginal information preserving  L-2,L-p-norm sparsity  compact representation  
SLAN: Similarity-aware aggregation network for embedding out-of-knowledge-graph entities 期刊论文
NEUROCOMPUTING, 2022, 卷号: 491, 页码: 186-196
作者:  Li, Mingda;  Sun, Zhengya;  Zhang, Wensheng
Adobe PDF(931Kb)  |  收藏  |  浏览/下载：314/52  |  提交时间：2022/06/10
Knowledge graph embedding  Out-of-knowledge-graph entities  Knowledge graph completion  Similarity search  
Inductive Spatiotemporal Graph Convolutional Networks for Short-term Quantitative Precipitation Forecasting 期刊论文
IEEE Transactions on Geoscience and Remote Sensing, 2022, 卷号: 0, 期号: 0, 页码: 0
作者:  Yajing, Wu;  Xuebing, Yang;  Yongqiang, Tang;  Chenyang, Zhang;  Guoping, Zhang;  Wensheng, Zhang
Adobe PDF(10052Kb)  |  收藏  |  浏览/下载：298/69  |  提交时间：2022/04/06
Quantitative precipitation forecasting  graph convolutional networks (GCN)  spatiotemporal model  radar-rain gauge data merging  
Partial Domain Adaptation by Progressive Sample Learning of Shared Classes 期刊论文
Neural Processing Letters, 2022, 卷号: 0, 期号: 0, 页码: 0
作者:  Lei, Tian;  Yongqiang, Tang;  Wensheng, Zhang
Adobe PDF(912Kb)  |  收藏  |  浏览/下载：202/52  |  提交时间：2022/04/06
Partial domain adaptation  Domain adaptation  Transfer learning  Self-paced learning  Low-dimensional subspace learning  
A New Subspace Clustering Strategy for AI-Based Data Analysis in IoT System 期刊论文
IEEE INTERNET OF THINGS JOURNAL, 2021, 卷号: 8, 期号: 16, 页码: 12540-12549
作者:  Cui, Zhihua;  Jing, Xuechun;  Zhao, Peng;  Zhang, Wensheng;  Chen, Jinjun
收藏  |  浏览/下载：214/0  |  提交时间：2021/11/02
Sparse matrices  Internet of Things  Clustering algorithms  Correlation  Artificial intelligence  Servers  Hyperspectral imaging  Close neighbors  data analysis  hyperspectral images (HSIs)  Internet of Things (IoT)  subspace clustering  
Deep convolutional self-paced clustering 期刊论文
APPLIED INTELLIGENCE, 2021, 页码: 15
作者:  Chen, Rui;  Tang, Yongqiang;  Tian, Lei;  Zhang, Caixia;  Zhang, Wensheng
Adobe PDF(2029Kb)  |  收藏  |  浏览/下载：257/48  |  提交时间：2021/11/02
Deep clustering  Convolutional autoencoder  Local structure preservation  Self-paced learning  
MEAD: a Mask-guidEd Anchor-free Detector for oriented aerial object detection 期刊论文
Applied Intelligence, 2021, 期号: 0, 页码: 0
作者:  Zewen He;  Zhida Ren;  Xuebing Yang;  Yang Yang;  Wensheng Zhang
Adobe PDF(4447Kb)  |  收藏  |  浏览/下载：225/50  |  提交时间：2021/06/28
Oriented aerial object detection  Anchor-free detector  Mask-guided mechanism  Cascade structure  
Improving Domain-adaptive Person Re-identification by Dual-alignment Learning with Camera-aware Image Generation 期刊论文
IEEE Transactions on Circuits and Systems for Video Technology, 2021, 卷号: 31, 期号: 11, 页码: 4334 - 4346
作者:  Chenyang Zhang;  Yongqiang Tang;  Zhizhong Zhang;  Ding Li;  Xuebing Yang;  Wensheng Zhang
Adobe PDF(2538Kb)  |  收藏  |  浏览/下载：338/83  |  提交时间：2021/06/23
Person Re-identification  Convolutional Neural Networks  Generative Adversarial Networks  Mutual Information  
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：346/72  |  提交时间：2021/01/06
Domain adaptation  class centroid matching  local manifold self-learning