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244 results on '"Zhang, Xuedian"'

1. Attention Auxiliary Supervision for Continuous Sign Language Recognition

Author

Qin, Xiaofei, Kong, Junyang, He, Changxiang, Zhang, Xuedian, Lua, Chong Ghee, Srigrarom, Sutthiphong, Khoo, Boo Cheong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Fenrong, editor, Sadanandan, Arun Anand, editor, Pham, Duc Nghia, editor, Mursanto, Petrus, editor, and Lukose, Dickson, editor

Published
2024
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6. Global-Temporal Enhancement for Sign Language Recognition

Author

Qin, Xiaofei, Wang, Hui, He, Changxiang, Zhang, Xuedian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Iliadis, Lazaros, editor, Papaleonidas, Antonios, editor, Angelov, Plamen, editor, and Jayne, Chrisina, editor

Published
2023
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9. Generalized Opinion Dynamics Model for Social Trust Networks in Opinion Maximization

Author

He, Changxiang, Zeng, Jiayuan, Liu, Shuting, Zhang, Guang, Qin, Xiaofei, Zhang, Xuedian, and Liu, Lele

Subjects
Mathematics - Combinatorics
Abstract

In this paper, we propose a generalized opinion dynamics model (GODM), which can dynamically compute each person's expressed opinion, to solve the internal opinion maximization problem for social trust networks. In the model, we propose a new, reasonable and interpretable confidence index, which is determined by both person's social status and the evaluation around him. By using the theory of diagonally dominant, we obtain the optimal analytic solution of the Nash equilibrium with maximum overall opinion. We design a novel algorithm to maximize the overall with given budget by modifying the internal opinions of people in the social trust network, and prove its optimality both from the algorithm itself and the traditional optimization algorithm-ADMM algorithms with $l_1$-regulations. A series of experiments are conducted, and the experimental results show that our method is superior to the state-of-the-art in four datasets. The average benefit has promoted $67.5\%$, $83.2\%$, $31.5\%$, and $33.7\%$ on four datasets, respectively.

Published
2021

14. Efficient High-Resolution Human Pose Estimation

Author

Qin, Xiaofei, Qiu, Lingfeng, He, Changxiang, Zhang, Xuedian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Khanna, Sankalp, editor, Cao, Jian, editor, Bai, Quan, editor, and Xu, Guandong, editor

Published
2022
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20. Single-Skeleton and Dual-Skeleton Hypergraph Convolution Neural Networks for Skeleton-Based Action Recognition

Author

He, Changxiang, Xiao, Chen, Liu, Shuting, Qin, Xiaofei, Zhao, Ying, Zhang, Xuedian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mantoro, Teddy, editor, Lee, Minho, editor, Ayu, Media Anugerah, editor, Wong, Kok Wai, editor, and Hidayanto, Achmad Nizar, editor

Published
2021
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21. DSNet: Dynamic Selection Network for Biomedical Image Segmentation

Author

Qin, Xiaofei, Liu, Yan, Tang, Liang, Zhao, Shuhui, Zhou, Xingchen, Zhang, Xuedian, Wang, Dengbin, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Farkaš, Igor, editor, Masulli, Paolo, editor, Otte, Sebastian, editor, and Wermter, Stefan, editor

Published
2021
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22. Structure-Aware Multi-scale Hierarchical Graph Convolutional Network for Skeleton Action Recognition

Author

He, Changxiang, Liu, Shuting, Zhao, Ying, Qin, Xiaofei, Zeng, Jiayuan, Zhang, Xuedian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Farkaš, Igor, editor, Masulli, Paolo, editor, Otte, Sebastian, editor, and Wermter, Stefan, editor

Published
2021
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42. Multimodal predictive classification of Alzheimer's disease based on attention‐combined fusion network: Integrated neuroimaging modalities and medical examination data.

Author

Chen, Hui, Guo, Huiru, Xing, Longqiang, Chen, Da, Yuan, Ting, Zhang, Yunpeng, and Zhang, Xuedian

Subjects
ALZHEIMER'S disease, PERIODIC health examinations, POSITRON emission tomography, MAGNETIC resonance imaging, IMAGE recognition (Computer vision), POSITRON emission, DATA fusion (Statistics)
Abstract

Early diagnosis of Alzheimer's disease (AD) plays a key role in preventing and responding to this neurodegenerative disease. It has shown that, compared with a single imaging modality‐based classification of AD, synergy exploration among multimodal neuroimages is beneficial for the pathological identification. However, effectively exploiting multimodal information is still a big challenge due to the lack of efficient fusion methods. Herein, a multimodal fusion network based on attention mechanism is proposed, in which magnetic resonance imaging (MRI) and positron emission computed tomography (PET) images are converted into feature vectors with the same dimension, while the demographic information and clinical data are preprocessed and converted into feature vectors through embedding. This attention model can focus on important feature points, fuse the multimodal information more effectively, and thus provide accurate diagnosis and prediction for different pathological stages. The results show that the model achieves an accuracy of 84.1% for triple classification tasks in normal cognition (NC) versus mild cognitive impairment (MCI) versus AD and 93.9% prediction accuracy in stable MCI (sMCI) versus progressive MCI (pMCI). In contrast to the existing multimodal diagnosis methods, our model yields a state‐of‐the‐art accuracy of AD diagnosis, which is powerful and promising in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2023
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48. A review of fiber‐coupled atomic magnetometer.

Author

Liu, Xuejing, Bi, Weihong, Li, Yang, Hu, Yanhui, Chang, Min, and Zhang, Xuedian

Subjects
MAGNETOMETERS, FLEXIBLE structures, MAGNETIC measurements, LARMOR precession, SPIN exchange, FIBERS, AMPLITUDE modulation
Abstract

Fiber‐coupled atomic magnetometer (AM) based on a spin‐exchange relaxation‐free (SERF) regime has been a promising candidate for cryogenic devices for traditional magnetoencephalography (MEG) systems, as it has a flexible structure and a small lateral section for biomedical applications. The principles, key technologies, and applications of fiber‐coupled AM based on the atomic spin effect are reviewed in this paper. The principle of fiber‐coupled AM is introduced, the characteristics of conventional and fiber‐coupled AMs are compared, and the development history and current situation are reviewed. In addition, according to the demands for MEG applications, the key technologies of fiber‐coupled AM are summarized, and the bottleneck problems and future development directions for weak magnetic measurement are analyzed. [ABSTRACT FROM AUTHOR]

Published
2023
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