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Narrowing the Gap: Improved Detector Training With Noisy Location Annotations 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 6369-6380
作者:  Wang, Shaoru;  Gao, Jin;  Li, Bing;  Hu, Weiming
Adobe PDF(1489Kb)  |  收藏  |  浏览/下载：186/23  |  提交时间：2022/11/14
Annotations  Noise measurement  Detectors  Task analysis  Training  Object detection  Degradation  Object detection  noisy label  Bayesian estimation  teacher-student learning  
Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 7458-7471
作者:  Han, Jiali;  Zhu, Lingjie;  Gao, Xiang;  Hu, Zhanyi;  Zhou, Liyang;  Liu, Hongmin;  Shen, Shuhan
Adobe PDF(8168Kb)  |  收藏  |  浏览/下载：240/32  |  提交时间：2021/11/03
Urban reconstruction  building modeling  Markov random field  segment based modeling  
An Iterative Co-Training Transductive Framework for Zero Shot Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 6943-6956
作者:  Liu, Bo;  Hu, Lihua;  Dong, Qiulei;  Hu, Zhanyi
Adobe PDF(2452Kb)  |  收藏  |  浏览/下载：225/52  |  提交时间：2021/11/02
Visualization  Semantics  Training  Feature extraction  Testing  Detectors  Predictive models  Zero-shot learning  transductive learning co-training  
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 期号: 29, 页码: 9532-9545
作者:  Shi, Lei;  Zhang, Yifan;  Cheng, Jian;  Lu, Hanqing
浏览  |  Adobe PDF(2849Kb)  |  收藏  |  浏览/下载：353/142  |  提交时间：2020/11/05
Skeleton-based action recognition, graph convolutional network, adaptive graph, multi-stream network.  
Tangent Fisher Vector on Matrix Manifolds for Action Recognition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 期号: 1, 页码: 3052-3064
作者:  Luo, Guan;  Wei, Jiutong;  Hu, Weiming;  Maybank, Stephen J.
浏览  |  Adobe PDF(2396Kb)  |  收藏  |  浏览/下载：332/75  |  提交时间：2020/04/07
Manifolds  Video sequences  Observability  Videos  Covariance matrices  Kernel  Computational modeling  Action recognition  Fisher vector  Grassmann manifold  Hankel matrix  matrix manifold  
Tensor Multi-Task Learning for Person Re-Identification 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 期号: 1, 页码: 2463-2477
作者:  Zhang, Zhizhong;  Xie, Yuan;  Zhang, Wensheng;  Tang, Yongqiang;  Tian, Qi
浏览  |  Adobe PDF(1444Kb)  |  收藏  |  浏览/下载：312/54  |  提交时间：2020/03/30
Cameras  Task analysis  Measurement  Visualization  Training  Computational modeling  Person re-identification  multi-task learning  tensor optimization  
Attention CoupleNet: Fully Convolutional Attention Coupling Network for Object Detection 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 1, 页码: 113 - 126
作者:  Zhu Yousong;  Zhao Chaoyang;  Guo Haiyun;  Wang Jinqiao;  Zhao Xu;  Lu Hanqing
浏览  |  Adobe PDF(2526Kb)  |  收藏  |  浏览/下载：628/235  |  提交时间：2019/05/04
Object Detection  Cascade Attention  Global Structure  Local Parts  
Beyond Group: Multiple Person Tracking via Minimal Topology-Energy-Variation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 卷号: 26, 期号: 12, 页码: 5575-5589
作者:  Gao, Shan;  Ye, Qixiang;  Xing, Junliang;  Kuijper, Arjan;  Han, Zhenjun;  Jiao, Jianbin;  Ji, Xiangyang
浏览  |  Adobe PDF(5475Kb)  |  收藏  |  浏览/下载：343/73  |  提交时间：2018/01/05
Multiple Person Tracking  Group Tracking  Rgb-d Data  Topology  
Conditional High-Order Boltzmann Machines for Supervised Relation Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 卷号: 26, 期号: 9, 页码: 4297-4310
作者:  Huang, Yan;  Wang, Wei;  Wang, Liang;  Tan, Tieniu
浏览  |  Adobe PDF(4175Kb)  |  收藏  |  浏览/下载：429/155  |  提交时间：2017/06/19
Deep Learning  High-order Boltzmann Machine  Relation Learning  Face Verification  Action Similarity Labeling  
Moving Object Detection Using Tensor-Based Low-Rank and Saliently Fused-Sparse Decomposition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 卷号: 26, 期号: 2, 页码: 724-737
作者:  Hu, Wenrui;  Yang, Yehui;  Zhang, Wensheng;  Xie, Yuan
Adobe PDF(13016Kb)  |  收藏  |  浏览/下载：488/195  |  提交时间：2017/09/12
Moving Object Detection  Tensor Nuclear Norm  Tensor Total Variation  Space-time Visual Saliency