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FDCPNet:feature discrimination and context propagation network for 3D shape representation
《虚拟现实与智能硬件(中英文)》2025年第1期83-94,共12页Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU 
Supported by the National Key R&D Program of China(2022YFC3803600).
Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or ...
关键词:3D shape representation Mesh model MeshNet Feature discrimination Context propagation 
Knowledge Graph Completion Method of Combining Structural Information with Semantic Information
《Chinese Journal of Electronics》2024年第6期1412-1420,共9页Binhao HU Jianpeng ZHANG Hongchang CHEN 
supported by the National Natural Science Foundation of China(Grant No.62002384);the General Project of China Postdoctoral Science Foundation(Grant No.2020M683760);the Songshan Laboratory Project(Grant No.221100210700-03)。
With the development of knowledge graphs,a series of applications based on knowledge graphs have emerged.The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality o...
关键词:Knowledge graph Knowledge graph completion Representation learning 
A general tail item representation enhancement framework for sequential recommendation被引量:1
《Frontiers of Computer Science》2024年第6期137-148,共12页Mingyue CHENG Qi LIU Wenyu ZHANG Zhiding LIU Hongke ZHAO Enhong CHEN 
the National Key R&D Program of China(No.2021YFF0901003)。
Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems(SRS).However,the current deep model structures are limited in their ability to learn high-...
关键词:sequential recommendation long-tail distribution training accelerating 
Representation strategy for unsupervised domain adaptation on person re-identification
《Optoelectronics Letters》2024年第12期749-756,共8页LI Hao ZHANG Tao LI Shuang LI Xuan ZHAO Xin 
The task of unsupervised person re-identification(Re-ID)is to transfer the knowledge learned in the source domain with no labels to the target domain with no labels.Due to the significant differences in the background...
关键词:NETWORKS branch branching 
Position-Aware and Subgraph Enhanced Dynamic Graph Contrastive Learning on Discrete-Time Dynamic Graph
《Computers, Materials & Continua》2024年第11期2895-2909,共15页Jian Feng Tian Liu Cailing Du 
Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of labels.However,existing studies neglect positional information ...
关键词:Dynamic graph representation learning graph contrastive learning structure representation position representation evolving pattern 
Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models
《虚拟现实与智能硬件(中英文)》2024年第5期383-395,共13页Robert KOSK Richard SOUTHERN Lihua YOU Shaojun BIAN Willem KOKKE Greg MAGUIRE 
Supported by the Centre for Digital Entertainment at Bournemouth University by the UK Engineering and Physical Sciences Research Council(EPSRC)EP/L016540/1 and Humain Ltd.
Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition sys...
关键词:Shape modelling Deep 3D morphable models Representation learning Feature engineering Perceptual metrics 
PUNet:A Semi-Supervised Anomaly Detection Model for Network Anomaly Detection Based on Positive Unlabeled Data
《Computers, Materials & Continua》2024年第10期327-343,共17页Gang Long Zhaoxin Zhang 
Network anomaly detection plays a vital role in safeguarding network security.However,the existing network anomaly detection task is typically based on the one-class zero-positive scenario.This approach is susceptible...
关键词:Network anomaly detection representation learning candidate set CatBoost 
Cross-modal learning using privileged information for long-tailed image classification
《Computational Visual Media》2024年第5期981-992,共12页Xiangxian Li Yuze Zheng Haokai Ma Zhuang Qi Xiangxu Meng Lei Meng 
supported in part by the National Natural Science Foundation of China(62006141);the National Key R&D Program of China(2021YFC3300203);the Overseas Innovation Team Project of the“20 Regulations for New Universities”Funding Program of Jinan(2021GXRC073);the Excellent Youth Scholars Program of Shandong Province(2022HWYQ-048).
The prevalence of long-tailed distributions in real-world data often results in classification models favoring the dominant classes,neglecting the less frequent ones.Current approaches address the issues in long-taile...
关键词:long-tailed classification cross-modal learning representation learning privileged infor-mation 
A Disentangled Representation-Based Multimodal Fusion Framework Integrating Pathomics and Radiomics for KRAS Mutation Detection in Colorectal Cancer被引量:1
《Big Data Mining and Analytics》2024年第3期590-602,共13页Zhilong Lv Rui Yan Yuexiao Lin Lin Gao Fa Zhang Ying Wang 
supported by the National Natural Science Foundation of China(Nos.61932018,32241027,62072441,62272326,62132015,and U22A2037);the Beijing Municipal Administration of Hospitals Incubating Program(No.PX2021013).
Kirsten rat sarcoma viral oncogene homolog(namely KRAS)is a key biomarker for prognostic analysis and targeted therapy of colorectal cancer.Recently,the advancement of machine learning,especially deep learning,has gre...
关键词:KRAS mutation detection multimodal feature fusion pathomics radiomics 
A Large-Scale Spatio-Temporal Multimodal Fusion Framework for Traffic Prediction被引量:1
《Big Data Mining and Analytics》2024年第3期621-636,共16页Bodong Zhou Jiahui Liu Songyi Cui Yaping Zhao 
Traffic prediction is crucial for urban planning and transportation management,and deep learning techniques have emerged as effective tools for this task.While previous works have made advancements,they often overlook...
关键词:SPATIO-TEMPORAL traffic prediction multimodal fusion learning representation 
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