Search

Your search keyword '"Niu, Junyu"' showing total 50 results
Start Over Author "Niu, Junyu"
50 results on '"Niu, Junyu"'

3. Multi-head Attentive Social Recommendation

Author

Luo, Xu, Sha, Chaofeng, Tan, Zijing, Niu, Junyu, 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, Cheng, Reynold, editor, Mamoulis, Nikos, editor, Sun, Yizhou, editor, and Huang, Xin, editor

Published
2019
Full Text
View/download PDF

4. Memory-Augmented Attention Network for Sequential Recommendation

Author

Hu, Cheng, He, Peijian, Sha, Chaofeng, Niu, Junyu, 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, Cheng, Reynold, editor, Mamoulis, Nikos, editor, Sun, Yizhou, editor, and Huang, Xin, editor

Published
2019
Full Text
View/download PDF

5. Hierarchical Parameter Sharing in Recursive Neural Networks with Long Short-Term Memory

Author

Li, Fengyu, Chi, Mingmin, Wu, Dong, Niu, Junyu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor

Published
2017
Full Text
View/download PDF

6. Improving Topic Diversity in Recommendation Lists: Marginally or Proportionally?

Author

Xing, Xiaolu, Sha, Chaofeng, Niu, Junyu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Lei, editor, Jensen, Christian S., editor, Shahabi, Cyrus, editor, Yang, Xiaochun, editor, and Lian, Xiang, editor

Published
2017
Full Text
View/download PDF

8. Action Recognition Based on GCN with Adjacency Matrix Generation Module and Time Domain Attention Mechanism.

Author

Yang, Rong, Niu, Junyu, Xu, Ying, Wang, Yun, and Qiu, Li

Subjects
*VIDEO compression, *RECOGNITION (Psychology), *DATA mining, *COMPUTER vision, *PHYSICAL constants, *VIDEO processing
Abstract

Different from other computer vision tasks, action recognition needs to process larger-scale video data. How to extract and analyze the effective parts from a huge amount of video information is the main difficulty of action recognition technology. In recent years, due to the outstanding performance of Graph Convolutional Networks (GCN) in many fields, a new solution to the action recognition algorithm has emerged. However, in current GCN models, the constant physical adjacency matrix makes it difficult to mine synergistic relationships between key points that are not directly connected in physical space. Additionally, a simple time connection of skeleton data from different frames makes each frame in the video contribute equally to the recognition results, which increases the difficulty of distinguishing action stages. In this paper, the information extraction ability of the model has been optimized in the space domain and time domain, respectively. In the space domain, an Adjacency Matrix Generation (AMG) module, which can pre-analyze node sets and generate an adaptive adjacency matrix, has been proposed. The adaptive adjacency matrix can help the graph convolution model to extract the synergistic information between the key points that are crucial for recognition. In the time domain, the Time Domain Attention (TDA) mechanism has been designed to calculate the time-domain weight vector through double pooling channels and complete the weights of key point sequences. Furthermore, performance of the improved TDA-AMG-GCN modules has been verified on the NTU-RGB+D dataset. Its detection accuracy at the CS and CV divisions reached 84.5% and 89.8%, respectively, with an average level higher than other commonly used detection methods at present. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

12. DivRec: A Framework for Top-N Recommendation with Diversification in E-commerce

Author

He, Kejun, Niu, Junyu, Sha, Chaofeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Chen, Lei, editor, Jia, Yan, editor, Sellis, Timos, editor, and Liu, Guanfeng, editor

Published
2014
Full Text
View/download PDF

14. Entropy Based Image Semantic Cycle for Image Classification

Author

Li, Hongyu, Niu, Junyu, Zhang, Lin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, Tingwen, editor, Zeng, Zhigang, editor, Li, Chuandong, editor, and Leung, Chi Sing, editor

Published
2012
Full Text
View/download PDF

15. Spatio-temporal LTSA and Its Application to Motion Decomposition

Author

Li, Hongyu, Niu, Junyu, Zhang, Lin, Hu, Bo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, Tingwen, editor, Zeng, Zhigang, editor, Li, Chuandong, editor, and Leung, Chi Sing, editor

Published
2012
Full Text
View/download PDF

16. Manifold Analysis of Spectral Munsell Colors

Author

Li, Hongyu, Lin, Chen, Niu, Junyu, Zhang, Lin, Parkkinen, Jussi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Huang, Tingwen, editor, Zeng, Zhigang, editor, Li, Chuandong, editor, and Leung, Chi Sing, editor

Published
2012
Full Text
View/download PDF

43. WIM at TREC 2007

Author

Xu, Jun, primary, Yao, Jing, primary, Zheng, Jiaqian, primary, Sun, Qi, primary, and Niu, Junyu, primary

Published
2007
Full Text
View/download PDF

47. WIM at TREC 2005

Author

Niu, Junyu, primary, Sun, Lin, primary, Lou, Luqun, primary, Deng, Fang, primary, Lin, Chen, primary, Zheng, Haiqing, primary, and Huang, Xuanjing, primary

Published
2005
Full Text
View/download PDF

48. Local tangent space based manifold entropy for image retrieval.

Author

Wang, Yi, Li, Hongyu, Niu, Junyu, and Zhang, Lin

Abstract

This paper proposes a new manifold entropy function based on local tangent space (LTS). With this entropy function, we further propose a framework for image retrieval. The retrieval is treated as searching for ordered cycles by categories in image datasets. The optimal cycles can be found by minimizing our manifold entropy of images. [ABSTRACT FROM PUBLISHER]

Published
2012

Catalog

Books, media, physical & digital resources
See catalog results