Yongxin Yang

2.0k total citations · 1 hit paper
10 papers, 654 citations indexed

About

Yongxin Yang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Yongxin Yang has authored 10 papers receiving a total of 654 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 2 papers in Aerospace Engineering. Recurrent topics in Yongxin Yang's work include Advanced Neural Network Applications (4 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Image and Signal Denoising Methods (2 papers). Yongxin Yang is often cited by papers focused on Advanced Neural Network Applications (4 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Image and Signal Denoising Methods (2 papers). Yongxin Yang collaborates with scholars based in China, United Kingdom and Sweden. Yongxin Yang's co-authors include Tao Xiang, Kaiyang Zhou, Andrea Cavallaro, Timothy M. Hospedales, Yang Yang, Zhigang Chu, Aleš Leonardis, Steven McDonagh, Nanqing Dong and Matteo Maggioni and has published in prestigious journals such as Journal of Sound and Vibration, Neurocomputing and ACM Transactions on Architecture and Code Optimization.

In The Last Decade

Yongxin Yang

9 papers receiving 631 citations

Hit Papers

Omni-Scale Feature Learning for Person Re-Identification 2019 2026 2021 2023 2019 100 200 300 400 500

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yongxin Yang China 5 571 195 83 55 43 10 654
Cheng-Hao Kuo United States 8 517 0.9× 72 0.4× 138 1.7× 49 0.9× 36 0.8× 14 558
Weijian Ruan China 11 551 1.0× 118 0.6× 85 1.0× 105 1.9× 12 0.3× 34 647
Xueping Wang China 13 298 0.5× 97 0.5× 61 0.7× 21 0.4× 18 0.4× 43 415
S.L. Dockstader United States 8 356 0.6× 91 0.5× 107 1.3× 61 1.1× 16 0.4× 18 420
Nima Razavi Switzerland 4 387 0.7× 43 0.2× 100 1.2× 65 1.2× 15 0.3× 4 452
Fengxiang Yang China 10 411 0.7× 195 1.0× 98 1.2× 19 0.3× 7 0.2× 21 523
Ancong Wu China 16 1.6k 2.9× 689 3.5× 150 1.8× 57 1.0× 20 0.5× 35 1.7k
Fida El Baf France 4 569 1.0× 48 0.2× 98 1.2× 66 1.2× 15 0.3× 6 617
Ya-Li Hou China 10 303 0.5× 34 0.2× 89 1.1× 48 0.9× 27 0.6× 26 448

Countries citing papers authored by Yongxin Yang

Since Specialization
Citations

This map shows the geographic impact of Yongxin Yang'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 Yongxin Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yongxin Yang more than expected).

Fields of papers citing papers by Yongxin Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yongxin Yang. 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 Yongxin Yang. The network helps show where Yongxin Yang may publish in the future.

Co-authorship network of co-authors of Yongxin Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Yongxin Yang. A scholar is included among the top collaborators of Yongxin Yang 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 Yongxin Yang. Yongxin Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Dong, Nanqing, Linus Ericsson, Yongxin Yang, Aleš Leonardis, & Steven McDonagh. (2024). Label-efficient object detection via region proposal network pre-training. Neurocomputing. 577. 127376–127376. 5 indexed citations
3.
Li, Jiansong, Xueying Wang, Xiaobing Chen, et al.. (2022). An Application-oblivious Memory Scheduling System for DNN Accelerators. ACM Transactions on Architecture and Code Optimization. 19(4). 1–26. 2 indexed citations
4.
Dong, Nanqing, Matteo Maggioni, Yongxin Yang, et al.. (2022). Residual Contrastive Learning for Image Reconstruction: Learning Transferable Representations from Noisy Images. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 2930–2936. 4 indexed citations
5.
Li, Jiansong, Guangli Li, Peng Zhao, et al.. (2021). Pinpointing the Memory Behaviors of DNN Training. 217–219. 2 indexed citations
6.
Sain, Aneeshan, Ayan Kumar Bhunia, Yongxin Yang, Tao Xiang, & Yi-Zhe Song. (2020). Cross-Modal Hierarchical Modelling for Fine-Grained Sketch Based Image Retrieval. 4 indexed citations
7.
Zhou, Kaiyang, Yongxin Yang, Andrea Cavallaro, & Tao Xiang. (2019). Omni-Scale Feature Learning for Person Re-Identification. View. 3701–3711. 581 indexed citations breakdown →
8.
Chu, Zhigang, et al.. (2018). Deconvolution using CLEAN-SC for acoustic source identification with spherical microphone arrays. Journal of Sound and Vibration. 440. 161–173. 31 indexed citations
9.
Yang, Yongxin & Timothy M. Hospedales. (2015). Deep Neural Networks for Sketch Recognition.. arXiv (Cornell University). 24 indexed citations
10.
Yang, Yongxin. (2007). RESEARCH PROGRESS OF CROSS CORRELATION ALGORITHMS IN PARTICLE IMAGE VELOCIMETRY. Lixue jinzhan. 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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