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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：376/77  |  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  
Efficient Center Voting for Object Detection and 6D Pose Estimation in 3D Point Cloud 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 期号: 1, 页码: 5072-5084
作者:  Guo, Jianwei;  Xing, Xuejun;  Quan, Weize;  Yan, Dong-Ming;  Gu, Qingyi;  Liu, Yang;  Zhang, Xiaopeng
Adobe PDF(8313Kb)  |  收藏  |  浏览/下载：206/3  |  提交时间：2021/08/15
Three-dimensional displays  Pose estimation  Shape  Object detection  Feature extraction  Object recognition  Transmission line matrix methods  6D pose estimation  3D object recognition  point pair features  3D point cloud  
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：187/46  |  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  
Multi-Domain Image-to-Image Translation via a Unified Circular Framework 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 期号: 30, 页码: 670-684
作者:  Wang, Yuxi;  Zhang, Zhaoxiang;  Hao, Wangli;  Song, Chunfeng
Adobe PDF(3399Kb)  |  收藏  |  浏览/下载：333/63  |  提交时间：2021/03/02
Task analysis  Semantics  Visualization  Generative adversarial networks  Generators  Feature extraction  Meteorology  Image-to-image transfer  sharing knowledge module  multiple domain pairs  GANs  
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：302/49  |  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  
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：358/72  |  提交时间：2021/01/06
Domain adaptation  class centroid matching  local manifold self-learning  
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：328/59  |  提交时间：2020/08/31
Person re-identification  part awareness  part segmentation  multi-task learning  
Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 7019-7031
作者:  Zhang, Yuyang;  Xu, Shibiao;  Wu, Baoyuan;  Shi, Jian;  Meng, Weiliang;  Zhang, Xiaopeng
Adobe PDF(8221Kb)  |  收藏  |  浏览/下载：317/70  |  提交时间：2020/08/03
Estimation  Training  Feature extraction  Geometry  Computer vision  Cameras  Unsupervised learning  Unsupervised learning  DenseDepthNet  multi-view geometry constraint  depth consistency  
Focal Boundary Guided Salient Object Detection 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 6, 页码: 2813-2824
作者:  Wang, Yupei;  Zhao, Xin;  Hu, Xuecai;  Li, Yin;  Huang, Kaiqi
浏览  |  Adobe PDF(3275Kb)  |  收藏  |  浏览/下载：593/265  |  提交时间：2019/04/19
Visual saliency detection  salient object segmentation  boundary detection  deep learning