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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：201/25  |  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：256/34  |  提交时间：2021/11/03
Urban reconstruction  building modeling  Markov random field  segment based modeling  
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：161/39  |  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  
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：317/63  |  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：293/54  |  提交时间：2020/08/31
Person re-identification  part awareness  part segmentation  multi-task learning  
A Performance Evaluation of Local Features for Image-Based 3D Reconstruction 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 10, 页码: 4774-4789
作者:  Fan, Bin;  Kong, Qingqun;  Wang, Xinchao;  Wang, Zhiheng;  Xiang, Shiming;  Pan, Chunhong;  Fua, Pascal
Adobe PDF(3986Kb)  |  收藏  |  浏览/下载：317/70  |  提交时间：2019/12/16
Local feature  image reconstruction  structure from motion (SFM)  3D vision  image matching  
Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 9, 页码: 4526-4540
作者:  Zhang, Hongwen;  Li, Qi;  Sun, Zhenan
Adobe PDF(4865Kb)  |  收藏  |  浏览/下载：303/41  |  提交时间：2019/10/08
2D/3D facial landmark localization  semantic volumetric representation  joint voxel and coordinate regression  auxiliary regression adversarial learning  
Attention-Based Pedestrian Attribute Analysis 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2019, 卷号: 28, 期号: 12, 页码: 6126-6140
作者:  Zichang Tan;  Yang Yang;  Jun Wan;  Hanyuan Hang;  Guodong Guo;  Stan Z. Li
Adobe PDF(3457Kb)  |  收藏  |  浏览/下载：199/37  |  提交时间：2020/10/27
Pedestrian attribute analysis  attention mechanism  pedestrian parsing  
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
Adobe PDF(2526Kb)  |  收藏  |  浏览/下载：635/235  |  提交时间：2019/05/04
Object Detection  Cascade Attention  Global Structure  Local Parts  
LFNet: A Novel Bidirectional Recurrent Convolutional Neural Network for Light-Field Image Super-Resolution 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 卷号: 27, 期号: 9, 页码: 4274-4286
作者:  Wang, Yunlong;  Liu, Fei;  Zhang, Kunbo;  Hou, Guangqi;  Sun, Zhenan;  Tan, Tieniu
Adobe PDF(4648Kb)  |  收藏  |  浏览/下载：329/23  |  提交时间：2018/10/10
Implicitly Multi-scale Fusion  Bidirectional Recurrent Convolutional Neural Network  Light-field  Super-resolution