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Global and local multi-modal feature mutual learning for retinal vessel segmentation 期刊论文
Pattern Recognition, 2024, 卷号: 151, 页码: 110376
作者:  Xin Zhao;  Zhang Jing;  Qiaozhe Li;  Tengfei Zhao;  Yi Li;  Zifeng Wu
Adobe PDF(4182Kb)  |  收藏  |  浏览/下载：47/18  |  提交时间：2024/06/21
Mutual learning  Multi-modal learning  OCTA images  Retinal vessel segmentation  
Explanation Guided Knowledge Distillation for Pre-trained Language Model Compression 期刊论文
ACM Transactions on Asian and Low-Resource Language Information Processing, 2024, 卷号: 23, 期号: 2, 页码: 1-19
作者:  Zhao Yang;  Yuanzhe Zhang;  Dianbo Sui;  Yiming Ju;  Jun Zhao;  Kang Liu
Adobe PDF(1250Kb)  |  收藏  |  浏览/下载：65/23  |  提交时间：2024/05/30
Explanation  knowledge distillation  model compression  
CLDRNet: A Difference Refinement Network Based on Category Context Learning for Remote Sensing Image Change Detection 期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 卷号: 17, 页码: 2133-2148
作者:  Wan, Ling;  Tian, Ye;  Kang, Wenchao;  Ma, Lei
Adobe PDF(15230Kb)  |  收藏  |  浏览/下载：108/6  |  提交时间：2024/02/20
Feature extraction  Task analysis  Remote sensing  Transformers  Deep learning  Semantics  Support vector machines  Category context learning (CCL)  clustering learning (CL)  difference map refinement (DMR)  optical remote sensing image  change detection (CD)  
Large sequence models for sequential decision-making: a survey 期刊论文
FRONTIERS OF COMPUTER SCIENCE, 2023, 卷号: 17, 期号: 6, 页码: 18
作者:  Wen, Muning;  Lin, Runji;  Wang, Hanjing;  Yang, Yaodong;  Wen, Ying;  Mai, Luo;  Wang, Jun;  Zhang, Haifeng;  Zhang, Weinan
Adobe PDF(1351Kb)  |  收藏  |  浏览/下载：164/9  |  提交时间：2023/11/17
sequential decision-making  sequence modeling  the Transformer  training system  
BSTG-Trans: A Bayesian Spatial-Temporal Graph Transformer for Long-term Pose Forecasting 期刊论文
IEEE Transactions on Multimedia, 2023, 卷号: Early Access, 期号: Early Access, 页码: Early Access
作者:  Shentong Mo;  Xin M(辛淼)
Adobe PDF(2209Kb)  |  收藏  |  浏览/下载：111/23  |  提交时间：2023/04/25
long-term forecasting  spatial-temporal graph transformer  Bayesian transformer  uncertainty estimation  
D-TNet: Category-Awareness Based Difference-Threshold Alternative Learning Network for Remote Sensing Image Change Detection 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 1-16
作者:  Wan, Ling;  Tian, Ye;  Kang, Wenchao;  Ma, Lei
Adobe PDF(8963Kb)  |  收藏  |  浏览/下载：158/42  |  提交时间：2023/03/20
Category-awareness  change detection  optical remote sensing image  threshold learning  
Global Instance Tracking: Locating Target More Like Humans 期刊论文
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 卷号: 45, 期号: 1, 页码: 576-592
作者:  Hu, Shiyu;  Zhao, Xin;  Huang, Lianghua;  Huang, Kaiqi
Adobe PDF(15055Kb)  |  收藏  |  浏览/下载：263/60  |  提交时间：2023/02/22
Global instance tracking  single object tracking  benchmark dataset  performance evaluation  human tracking ability  
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：312/17  |  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：234/13  |  提交时间：2022/07/25
scoliosis  Cobb angle measurement  deep learning  image enhancement  
FedFV: federated face verification via equivalent class embeddings 期刊论文
MULTIMEDIA SYSTEMS, 2022, 页码: 11
作者:  Liu, Lingyun;  Zhang, Yifan;  Gao, Haoyuan;  Yu, Xingtao;  Cheng, Jian
Adobe PDF(1435Kb)  |  收藏  |  浏览/下载：416/83  |  提交时间：2022/06/10
Equivalent class embeddings  Federated learning  Face recognition  Deep learning