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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：281/37  |  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：324/43  |  提交时间：2021/11/03
Urban reconstruction  building modeling  Markov random field  segment based modeling  
Multi-Scale Low-Discriminative Feature Reactivation for Weakly Supervised Object Localization 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 6050-6065
作者:  Wang, Bo;  Yuan, Chunfeng;  Li, Bing;  Ding, Xinmiao;  Li, Zeya;  Wu, Ying;  Hu, Weiming
收藏  |  浏览/下载：279/0  |  提交时间：2021/08/15
Location awareness  Neurons  Task analysis  Pattern recognition  Automation  Search problems  Proposals  Weakly supervised object localization  feature recalibration  multi-scale class activation mapping  
Anisotropic Convolution for Image Classification 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 期号: 99, 页码: 5584-5595
作者:  Li, Wenjuan;  Li, Bing;  Yuan, Chunfeng;  Li, Yangxi;  Wu, Haohao;  Hu, Weiming;  Wang, Fangshi
收藏  |  浏览/下载：191/0  |  提交时间：2020/10/16
Anisotropic convolution  image classification  object localization  
Multi-Cue Semi-Supervised Color Constancy With Limited Training Samples 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 7875-7888
作者:  Huang, Xinwei;  Li, Bing;  Li, Shuai;  Li, Wenjuan;  Xiong, Weihua;  Yin, Xuanwu;  Hu, Weiming;  Qin, Hong
收藏  |  浏览/下载：233/0  |  提交时间：2020/08/24
Color constancy  illumination estimation  white balancing  multi-cue  semi-supervised  
Anisotropic Convolution for Image Classification 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 5584-5595
作者:  Li, Wenjuan;  Li, Bing;  Yuan, Chunfeng;  Li, Yangxi;  Wu, Haohao;  Hu, Weiming;  Wang, Fangshi
收藏  |  浏览/下载：298/0  |  提交时间：2020/06/22
Convolution  Shape  Kernel  Feature extraction  Task analysis  Training  Neural networks  Anisotropic convolution  image classification  object localization  
STA-CNN: Convolutional Spatial-Temporal Attention Learning for Action Recognition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 5783-5793
作者:  Yang, Hao;  Yuan, Chunfeng;  Zhang, Li;  Sun, Yunda;  Hu, Weiming;  Maybank, Stephen J.
收藏  |  浏览/下载：378/0  |  提交时间：2020/06/22
Videos  Feature extraction  Motion segmentation  Computational modeling  Image recognition  Solid modeling  Convolutional neural networks  Temporal attention  spatial attention  convolutional neural network  action recognition  
Efficient Single-Stage Pedestrian Detector by Asymptotic Localization Fitting and Multi-Scale Context Encoding 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 1413-1425
作者:  Liu, Wei;  Liao, Shengcai;  Hu, Weidong
收藏  |  浏览/下载：223/0  |  提交时间：2020/03/30
Pedestrian detection  convolutional neural networks  asymptotic localization fitting  
Context-Dependent Random Walk Graph Kernels and Tree Pattern Graph Matching Kernels With Applications to Action Recognition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2018, 卷号: 27, 期号: 10, 页码: 5060-5075
作者:  Hu, Weiming;  Wu, Baoxin;  Wang, Pei;  Yuan, Chunfeng;  Li, Yangxi;  Maybank, Stephen
收藏  |  浏览/下载：273/0  |  提交时间：2019/12/16
Visual action recognition  graph kernel  graph matching  contexts  tree patterns  
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：617/276  |  提交时间：2019/04/19
Visual saliency detection  salient object segmentation  boundary detection  deep learning