中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共5条，第1-5条 帮助

限定条件    
已选(0)清除 条数/页：   排序方式：
Automatically Discovering Novel Visual Categories With Adaptive Prototype Learning 期刊论文
https://ieeexplore.ieee.org/abstract/document/10328468, 2024, 卷号: 46, 期号: 4, 页码: 2533-2544
作者:  Lu Zhang;  Lu Qi;  Xu Yang;  Hong Qiao;  Ming-Hsuan Yang;  Zhiyong Liu
Adobe PDF(20312Kb)  |  收藏  |  浏览/下载：47/17  |  提交时间：2024/06/06
Image recognition  novel category discovery  transfer learning  
Multi-Prototype Guided Source-Free Domain Adaptive Object Detection for Autonomous Driving 期刊论文
https://ieeexplore.ieee.org/document/10336548/, 2024, 卷号: 9, 期号: 1, 页码: 1589-1601
作者:  Siqi Zhang;  Lu Zhang;  Guangsen Li;  Pengcheng Li;  Zhiyong Liu
Adobe PDF(4349Kb)  |  收藏  |  浏览/下载：46/15  |  提交时间：2024/06/06
Prototypes  Labeling  Object detection  Adaptation models  Detectors  Noise measurement  Task analysis  Object detection  self training  source-free domain adaptation  transfer learning  
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：300/16  |  提交时间：2021/11/03
Skeleton-based action recognition  graph convolutional network  lightweight network  shift network  
A hybrid convolutional architecture for accurate image manipulation localization at the pixel-level 期刊论文
MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 期号: 80, 页码: 23377–23392
作者:  Zhang, Yixuan;  Zhang, Jiguang;  Xu, Shibiao
Adobe PDF(1369Kb)  |  收藏  |  浏览/下载：314/67  |  提交时间：2021/03/08
Manipulation localization  Top-down detection  Bottom-up segmentation  DenseCRFs  
Gesture recognition based on deep deformable 3D convolutional neural networks 期刊论文
PATTERN RECOGNITION, 2020, 期号: 107, 页码: 12
作者:  Zhang, Yifan;  Shi, Lei;  Wu, Yi;  Cheng, Ke;  Cheng, Jian;  Lu, Hanqing
Adobe PDF(1310Kb)  |  收藏  |  浏览/下载：504/143  |  提交时间：2020/08/31
Gesture recognition  Spatiotemporal deformable convolution  Spatiotemporal convolutional neural network