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TR-GAN: Multi-session future MRI prediction with temporal recurrent generative adversarial network 期刊论文
IEEE Transactions on Medical Imaging, 2022, 卷号: vol. 41, 期号: no. 8, 页码: 1925-1937
作者:  Fan, Chen-Chen;  Peng, Liang;  Wang, Tian;  Yang, Hongjun;  Zhou, Xiao-Hu;  Ni, Zhen-Liang;  Wang, Guan’an;  Chen, Sheng;  Zhou, Yan-Jie;  Hou, Zeng-Guang
Adobe PDF(11092Kb)  |  收藏  |  浏览/下载：232/25  |  提交时间：2023/06/02
Alzheimer’s disease  magnetic resonance imaging  generative adversarial network  
Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2022, 卷号: 32, 期号: 10, 页码: 6728-6740
作者:  Liu, Jierui;  Cao, Zhiqiang;  Tang, Yingbo;  Liu, Xilong;  Tan, Min
Adobe PDF(22124Kb)  |  收藏  |  浏览/下载：277/10  |  提交时间：2022/11/14
Shape  Three-dimensional displays  Cognition  Pose estimation  Feature extraction  Decoding  Solid modeling  Category-level  6D object pose estimation  structure encoder  reasoning attention  
A Review of the Methods on Cobb Angle Measurements for Spinal Curvature 期刊论文
SENSORS, 2022, 卷号: 22, 期号: 9, 页码: 19
作者:  Jin, Chen;  Wang, Shengru;  Yang, Guodong;  Li, En;  Liang, Zize
Adobe PDF(4721Kb)  |  收藏  |  浏览/下载：195/1  |  提交时间：2022/07/25
scoliosis  Cobb angle measurement  deep learning  image enhancement  
Temporal-adaptive sparse feature aggregation for video object detection 期刊论文
PATTERN RECOGNITION, 2022, 卷号: 127, 页码: 108587
作者:  He, Fei;  Li, Qiaozhe;  Zhao, Xin;  Huang, Kaiqi
Adobe PDF(1549Kb)  |  收藏  |  浏览/下载：328/44  |  提交时间：2022/06/10
Video object detection  Temporal-adaptive sparse sampling  Pixel-adaptive aggregation  Object-relational aggregation  
Learning Accurate and Stable Point-to-Point Motions: A Dynamic System Approach 期刊论文
IEEE ROBOTICS AND AUTOMATION LETTERS, 2022, 卷号: 7, 期号: 2, 页码: 1510-1517
作者:  Zhang, Yu;  Cheng, Long;  Li, Houcheng;  Cao, Ran
Adobe PDF(3334Kb)  |  收藏  |  浏览/下载：244/21  |  提交时间：2022/02/16
Point-to-point tasks  neural network  dynamic system  generalization performance  high dimensional data