Xueping Su
Northwestern Polytechnical University
10 Papers
12 Citations
Xueping Su is an academic researcher from Northwestern Polytechnical University. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 2, co-authored 2 publications.
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Papers
MRGCN: cancer subtyping with multi-reconstruction graph convolutional network using full and partial multi-omics dataset
Bo Yang,Meng Wang,Xueping Su +2 more
TL;DR: In this paper , a graph convolutional network model is proposed for multi-omics data integrative representation, which simultaneously encodes and reconstructs multiple omics expression and similarity relationships into a shared latent embedding space.
Cross-modality based celebrity face naming for news image collections
TL;DR: By combining the domain knowledge from the captions and the corresponding image, a novel cross-modality approach is proposed to further improve the performance of linking names with faces from large-scale news images with captions.
10
Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph
TL;DR: A new algorithm of personalized Chinese tourism recommendation based on the Knowledge Graph with significant improvement over the existing algorithms is proposed and the method of user interest modelbased on the attribute information of users and tourist attractions is proposed to improve the performance of the recommendation system.
Face Shape Classification Based on Bilinear Network with Attention Mechanism
TL;DR: Experiments on public data sets show that the algorithm proposed is get state-of-the-art results and significantly improves the accuracy of face shape classification.
6
DIR-SLAM: Dynamic Interference Removal for Real-Time VSLAM in Dynamic Environments
Xiaomin Ma,Yeong-Il Yang,Lei Zhu,Ying Min Yi,Jing Xin,Xueping Su,Minqi Li +6 more
TL;DR: Zhang et al. as discussed by the authors proposed a real-time and robust dynamic interference removal (DIR) method, which is based on both prior knowledge and geometry information, which employs a novel lightweight CNN network to output semantic labels and extends the semantics based on the correlations of descriptors to generate a segmented mask.