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Mixed-Supervised Scene Text Detection With Expectation-Maximization Algorithm 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 5513-5528
作者:  Zhao, Mengbiao;  Feng, Wei;  Yin, Fei;  Zhang, Xu-Yao;  Liu, Cheng-Lin
Adobe PDF(5999Kb)  |  收藏  |  浏览/下载：297/35  |  提交时间：2022/09/19
Costs  Annotations  Training  Labeling  Detectors  Data models  Benchmark testing  Mixed-supervised learning  scene text detection  weak supervision forms  expectation-maximization algorithm  
Learning Aligned Image-Text Representations Using Graph Attentive Relational Network 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 期号: 30, 页码: 1840-1852
作者:  Jing, Ya;  Wang, Wei;  Wang, Liang;  Tan, Tieniu
Adobe PDF(4532Kb)  |  收藏  |  浏览/下载：312/50  |  提交时间：2021/03/08
Graph neural networks  Visualization  Semantics  Task analysis  Feature extraction  Annotations  Recurrent neural networks  Image-text matching  cross-modal retrieval  person search  graph neural network  
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：256/34  |  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：238/52  |  提交时间：2021/11/02
Visualization  Semantics  Training  Feature extraction  Testing  Detectors  Predictive models  Zero-shot learning  transductive learning co-training  
Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN plus 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 7333-7348
作者:  Cheng, Ke;  Zhang, Yifan;  He, Xiangyu;  Cheng, Jian;  Lu, Hanqing
Adobe PDF(3205Kb)  |  收藏  |  浏览/下载：247/13  |  提交时间：2021/11/03
Skeleton-based action recognition  graph convolutional network  lightweight network  shift network  
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 期号: 0, 页码: 6013
作者:  Gao, Naiyu;  Shan, Yanhu;  Zhao, Xin;  Huang, Kaiqi
Adobe PDF(3484Kb)  |  收藏  |  浏览/下载：171/38  |  提交时间：2022/06/14
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：367/144  |  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：346/76  |  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：330/57  |  提交时间：2020/03/30
Cameras  Task analysis  Measurement  Visualization  Training  Computational modeling  Person re-identification  multi-task learning  tensor optimization  
Improve Person Re-Identification With Part Awareness Learning 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 7468-7481
作者:  Huang, Houjing;  Yang, Wenjie;  Lin, Jinbin;  Huang, Guan;  Xu, Jiamiao;  Wang, Guoli;  Chen, Xiaotang;  Huang, Kaiqi
Adobe PDF(3927Kb)  |  收藏  |  浏览/下载：296/54  |  提交时间：2020/08/31
Person re-identification  part awareness  part segmentation  multi-task learning