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Correntropy-Induced Wasserstein GCN: Learning Graph Embedding via Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 页码: 3980-3993
作者:  Wei Wang;  Gaowei Zhang;  Hongyong Han;  Chi Zhang
Adobe PDF(8686Kb)  |  收藏  |  浏览/下载：20/8  |  提交时间：2024/06/27
Coarse Mask Guided Interactive Object Segmentation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 5808-5822
作者:  Li, Jing;  Fan, Junsong;  Wang, Yuxi;  Yang, Yuran;  Zhang, Zhaoxiang
Adobe PDF(4323Kb)  |  收藏  |  浏览/下载：63/4  |  提交时间：2024/02/22
Segmentation  interactive  transformer  annotation tool  
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：463/42  |  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  
An Efficient Sampling-Based Attention Network for Semantic Segmentation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 2850-2863
作者:  He, Xingjian;  Liu, Jing;  Wang, Weining;  Lu, Hanqing
Adobe PDF(3252Kb)  |  收藏  |  浏览/下载：398/80  |  提交时间：2022/06/10
Stochastic processes  Sampling methods  Semantics  Image segmentation  Computational complexity  Pattern recognition  Convolution  Semantic segmentation  stochastic sampling-based attention  deterministic sampling-based attention  
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：324/43  |  提交时间：2021/11/03
Urban reconstruction  building modeling  Markov random field  segment based modeling  
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：297/16  |  提交时间：2021/11/03
Skeleton-based action recognition  graph convolutional network  lightweight network  shift network  
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：297/66  |  提交时间：2021/11/02
Visualization  Semantics  Training  Feature extraction  Testing  Detectors  Predictive models  Zero-shot learning  transductive learning co-training  
Attention-Based Multi-Source Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 3793-3803
作者:  Zuo, Yukun;  Yao, Hantao;  Xu, Changsheng
收藏  |  浏览/下载：283/0  |  提交时间：2021/05/06
Correlation  Adaptation models  Feature extraction  Target recognition  Data models  Transfer learning  Visualization  Multi-source domain adaptation  attention-based multi-source domain adaptation  weighted moment distance  
Nocal-Siam: Refining Visual Features and Response With Advanced Non-Local Blocks for Real-Time Siamese Tracking 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 2656-2668
作者:  Tan, Huibin;  Zhang, Xiang;  Zhang, Zhipeng;  Lan, Long;  Zhang, Wenju;  Luo, Zhigang
Adobe PDF(2898Kb)  |  收藏  |  浏览/下载：215/60  |  提交时间：2021/03/29
Target tracking  Visualization  Feature extraction  Real-time systems  Oceans  Convolution  Task analysis  Siamese trackers  non-local attention  supervisedly attentive  
Attention Guided Multiple Source and Target Domain Adaptation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 期号: 30, 页码: 892-906
作者:  Wang, Yuxi;  Zhang, Zhaoxiang;  Hao, Wangli;  Song, Chunfeng
Adobe PDF(3460Kb)  |  收藏  |  浏览/下载：321/53  |  提交时间：2021/03/02
Semantics  Task analysis  Generators  Generative adversarial networks  Feature extraction  Visualization  Meteorology  Domain adaptation  multiple source and target domains  attention