Richard Yi Da Xu

1.8k total citations
107 papers, 1.1k citations indexed

About

Richard Yi Da Xu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Richard Yi Da Xu has authored 107 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Computer Vision and Pattern Recognition, 45 papers in Artificial Intelligence and 11 papers in Computer Networks and Communications. Recurrent topics in Richard Yi Da Xu's work include Video Surveillance and Tracking Methods (20 papers), Advanced Vision and Imaging (17 papers) and Advanced Image and Video Retrieval Techniques (15 papers). Richard Yi Da Xu is often cited by papers focused on Video Surveillance and Tracking Methods (20 papers), Advanced Vision and Imaging (17 papers) and Advanced Image and Video Retrieval Techniques (15 papers). Richard Yi Da Xu collaborates with scholars based in Australia, China and Hong Kong. Richard Yi Da Xu's co-authors include John G. Allen, Jesse S. Jin, J. Andrew Zhang, Zhenguo Shi, Qingqing Cheng, Massimo Piccardi, Xiangfeng Luo, Yang Li, Longbing Cao and Ziyue Zhang and has published in prestigious journals such as IEEE Transactions on Image Processing, IEEE Transactions on Signal Processing and IEEE Access.

In The Last Decade

Richard Yi Da Xu

100 papers receiving 1.0k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Richard Yi Da Xu Australia 18 592 293 175 108 107 107 1.1k
Zhonglong Zheng China 20 720 1.2× 337 1.2× 121 0.7× 119 1.1× 85 0.8× 144 1.4k
Piyush Kumar United States 17 427 0.7× 238 0.8× 247 1.4× 113 1.0× 91 0.9× 62 1.3k
Gang Qian United States 18 749 1.3× 273 0.9× 102 0.6× 90 0.8× 166 1.6× 76 1.4k
Jia Xu China 18 428 0.7× 500 1.7× 142 0.8× 87 0.8× 74 0.7× 88 1.3k
Jianhua Zou China 16 568 1.0× 329 1.1× 176 1.0× 203 1.9× 57 0.5× 60 1.2k
Naim Dahnoun United Kingdom 17 572 1.0× 190 0.6× 300 1.7× 68 0.6× 171 1.6× 77 1.4k
Yiguang Liu China 23 634 1.1× 398 1.4× 203 1.2× 239 2.2× 52 0.5× 106 1.5k
Wei Niu United States 16 542 0.9× 314 1.1× 167 1.0× 83 0.8× 109 1.0× 100 1.0k
Mohamed Batouche Algeria 18 367 0.6× 475 1.6× 85 0.5× 124 1.1× 53 0.5× 116 1.2k

Countries citing papers authored by Richard Yi Da Xu

Since Specialization
Citations

This map shows the geographic impact of Richard Yi Da Xu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Richard Yi Da Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Yi Da Xu more than expected).

Fields of papers citing papers by Richard Yi Da Xu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard Yi Da Xu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Richard Yi Da Xu. The network helps show where Richard Yi Da Xu may publish in the future.

Co-authorship network of co-authors of Richard Yi Da Xu

This figure shows the co-authorship network connecting the top 25 collaborators of Richard Yi Da Xu. A scholar is included among the top collaborators of Richard Yi Da Xu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Richard Yi Da Xu. Richard Yi Da Xu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wang, Kai, et al.. (2025). KERMIT: Knowledge graph completion of enhanced relation modeling with inverse transformation. Knowledge-Based Systems. 324. 113500–113500. 1 indexed citations
2.
Niu, Shuai, Jing Ma, Yunya Song, et al.. (2024). Enhancing healthcare decision support through explainable AI models for risk prediction. Decision Support Systems. 181. 114228–114228. 10 indexed citations
3.
Zhou, Shengchao, Gaofeng Meng, Zhaoxiang Zhang, Richard Yi Da Xu, & Shiming Xiang. (2023). Robust Feature Rectification of Pretrained Vision Models for Object Recognition. Proceedings of the AAAI Conference on Artificial Intelligence. 37(3). 3796–3804.
4.
Zhang, J. Andrew, et al.. (2023). Multi-Object Tracking with mmWave Radar: A Review. Electronics. 12(2). 308–308. 28 indexed citations
5.
Huang, Wei, et al.. (2023). Implicit Bias of Deep Learning in the Large Learning Rate Phase: A Data Separability Perspective. Applied Sciences. 13(6). 3961–3961. 1 indexed citations
6.
Shi, Zhenguo, Qingqing Cheng, J. Andrew Zhang, & Richard Yi Da Xu. (2022). Environment-Robust WiFi-Based Human Activity Recognition Using Enhanced CSI and Deep Learning. IEEE Internet of Things Journal. 9(24). 24643–24654. 36 indexed citations
7.
Xie, Hong-Bo, et al.. (2020). Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures. IEEE Transactions on Signal Processing. 68. 3860–3870. 11 indexed citations
8.
Shi, Zhenguo, J. Andrew Zhang, Richard Yi Da Xu, & Qingqing Cheng. (2020). Environment-Robust Device-Free Human Activity Recognition With Channel-State-Information Enhancement and One-Shot Learning. IEEE Transactions on Mobile Computing. 21(2). 540–554. 67 indexed citations
9.
Xu, Richard Yi Da, et al.. (2019). Realistic Image Generation using Region-phrase Attention. Asian Conference on Machine Learning. 284–299. 2 indexed citations
10.
Xie, Hong-Bo, et al.. (2019). Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator. IEEE Transactions on Image Processing. 28(10). 4899–4911. 11 indexed citations
11.
Li, Minqi, et al.. (2019). Fast non-rigid points registration with cluster correspondences projection. Signal Processing. 170. 107425–107425. 3 indexed citations
12.
Xu, Richard Yi Da, et al.. (2019). Efficient Diversified Mini-Batch Selection using Variable High-layer Features. Asian Conference on Machine Learning. 300–315. 1 indexed citations
13.
Xu, Richard Yi Da, et al.. (2018). Semantic Emotion-Topic Model Based Social Emotion Mining.. Journal of Web Engineering. 17. 73–92. 2 indexed citations
14.
Li, Kan, et al.. (2018). Relative Pairwise Relationship Constrained Non-Negative Matrix Factorisation. IEEE Transactions on Knowledge and Data Engineering. 31(8). 1595–1609. 6 indexed citations
15.
Yang, Wankou, Jun Li, Hao Zheng, & Richard Yi Da Xu. (2017). A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction. IEEE Access. 6. 7445–7451. 13 indexed citations
16.
Xu, Richard Yi Da, et al.. (2016). Copula mixed-membership stochastic blockmodel. UTS ePRESS (University of Technology Sydney). 1462–1468. 7 indexed citations
17.
Xu, Richard Yi Da, et al.. (2014). A non-parametric conditional factor regression model for multi-dimensional input and response. Cambridge University Engineering Department Publications Database. 77–85. 5 indexed citations
18.
Xu, Richard Yi Da, et al.. (2009). Fragment size detection within homogeneous material using Ground Penetrating Radar. Charles Sturt University Research Output (CRO). 1–5. 1 indexed citations
19.
Xu, Richard Yi Da, John G. Allen, & Jesse S. Jin. (2005). Framework for Multimedia Lecture System using Real-Time Video Object Event Detection and Scripting.. 349–352. 1 indexed citations
20.
Xu, Richard Yi Da, et al.. (2005). E-Learning via Augmented Reality on Adaptive LMS. E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. 2005(1). 2199–2204. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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