中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共9条，第1-9条 帮助

限定条件                            
已选(0)清除 条数/页：   排序方式：
Manipulating Template Pixels for Model Adaptation of Siamese Visual Tracking 期刊论文
IEEE SIGNAL PROCESSING LETTERS, 2020, 卷号: 27, 页码: 1690-1694
作者:  Li, Zhenbang;  Li, Bing;  Gao, Jin;  Li, Liang;  Hu, Weiming
收藏  |  浏览/下载：205/0  |  提交时间：2021/01/07
Adaptation models  Target tracking  Task analysis  Visualization  Feature extraction  Object tracking  Training  Model adaptation  siamese networks  visual tracking  
Distractor-aware discrimination learning for online multiple object tracking 期刊论文
PATTERN RECOGNITION, 2020, 期号: 107, 页码: 10
作者:  Zhou, Zongwei;  Luo, Wenhan;  Wang, Qiang;  Xing, Junliang;  Hu, Weiming
Adobe PDF(840Kb)  |  收藏  |  浏览/下载：393/112  |  提交时间：2020/08/31
Multi-object tracking  Distractor-aware discrimination learning  Relational attention learning  
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：210/0  |  提交时间：2020/08/24
Color constancy  illumination estimation  white balancing  multi-cue  semi-supervised  
Graph convolutional network with structure pooling and joint-wise channel attention for action recognition 期刊论文
PATTERN RECOGNITION, 2020, 卷号: 103, 页码: 12
作者:  Chen, Yuxin;  Ma, Gaoqun;  Yuan, Chunfeng;  Li, Bing;  Zhang, Hui;  Wang, Fangshi;  Hu, Weiming
收藏  |  浏览/下载：261/0  |  提交时间：2020/06/22
Graph convolutional network  Structure graph pooling  Joint-wise channel attention  
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：276/0  |  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：325/0  |  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  
Tracking-by-Fusion via Gaussian Process Regression Extended to Transfer Learning 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 卷号: 42, 期号: 4, 页码: 939-955
作者:  Gao, Jin;  Wang, Qiang;  Xing, Junliang;  Ling, Haibin;  Hu, Weiming;  Maybank, Stephen
收藏  |  浏览/下载：284/0  |  提交时间：2020/06/02
Task analysis  Correlation  Target tracking  Probability distribution  Visualization  Collaboration  Visual tracking  Gaussian processes  correlation filters  transfer learning  tracking-by-fusion  
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：366/80  |  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  
Anomaly Detection Using Local Kernel Density Estimation and Context-Based Regression 期刊论文
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2020, 卷号: 32, 期号: 2, 页码: 218-233
作者:  Hu, Weiming;  Gao, Jun;  Li, Bing;  Wu, Ou;  Du, Junping;  Maybank, Stephen
收藏  |  浏览/下载：292/0  |  提交时间：2020/03/30
Anomaly detection  Kernel  Estimation  Saliency detection  Visualization  Data models  Computational modeling  Anomaly detection  local kernel density estimation  weighted neighborhood density  hierarchical context-based local kernel regression