中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共4条，第1-4条 帮助

限定条件            
已选(0)清除 条数/页：   排序方式：
A Novel Biologically Inspired Structural Model for Feature Correspondence 期刊论文
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2023, 卷号: 15, 期号: 2, 页码: 844-854
作者:  Lu, Yan-Feng;  Yang, Xu;  Li, Yi;  Yu, Qian;  Liu, Zhi-Yong;  Qiao, Hong
Adobe PDF(4447Kb)  |  收藏  |  浏览/下载：148/2  |  提交时间：2023/11/17
Visualization  Biological system modeling  Biology  Brain modeling  Biological information theory  Task analysis  Strain  Appearance feature descriptor  biologically inspired model  feature correspondence  feature representation  graph matching (GM)  graph structure  
sEMG-Based Gesture Recognition Method for Coal Mine Inspection Manipulator Using Multistream CNN 期刊论文
IEEE SENSORS JOURNAL, 2023, 卷号: 23, 期号: 10, 页码: 11082-11090
作者:  Tong, Lina;  Zhang, Mingjia;  Ma, Hanghang;  Wang, Chen;  Peng, Liang
Adobe PDF(2841Kb)  |  收藏  |  浏览/下载：91/3  |  提交时间：2023/11/17
Sensors  Muscles  Inspection  Coal mining  Robots  Feature extraction  Gesture recognition  Coal mine inspection manipulator  gestures recognition  multistream convolutional neural network (CNN)  surface electromyography (sEMG)  time--frequency graph feature  
Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2023, 卷号: 33, 期号: 9, 页码: 4616-4629
作者:  Ma, Chengcheng;  Liu, Yang;  Deng, Jiankang;  Xie, Lingxi;  Dong, Weiming;  Xu, Changsheng
Adobe PDF(1644Kb)  |  收藏  |  浏览/下载：125/17  |  提交时间：2023/11/16
Vision-language model  prompt tuning  over-fitting  subspace learning  gradient projection  
Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework 期刊论文
COMPUTERS IN BIOLOGY AND MEDICINE, 2023, 卷号: 165, 页码: 11
作者:  Zhang, Jiaxin;  Wei, Zechen;  Wu, Xiangjun;  Shang, Yaxin;  Tian, Jie;  Hui, Hui
Adobe PDF(6341Kb)  |  收藏  |  浏览/下载：157/5  |  提交时间：2023/11/16
Magnetic particle imaging  Deblurring  Unpaired data  Contrastive learning  Adversarial framework