Xiaofeng Ren

1.2k total citations · 1 hit paper
9 papers, 790 citations indexed

About

Xiaofeng Ren is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Xiaofeng Ren has authored 9 papers receiving a total of 790 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 2 papers in Media Technology. Recurrent topics in Xiaofeng Ren's work include Advanced Image and Video Retrieval Techniques (4 papers), Image Retrieval and Classification Techniques (3 papers) and Advanced Vision and Imaging (2 papers). Xiaofeng Ren is often cited by papers focused on Advanced Image and Video Retrieval Techniques (4 papers), Image Retrieval and Classification Techniques (3 papers) and Advanced Vision and Imaging (2 papers). Xiaofeng Ren collaborates with scholars based in United States, Germany and Switzerland. Xiaofeng Ren's co-authors include Liefeng Bo, D. Fox, Deva Ramanan, Feng Liu, Lu Si, Andrea Fossati, Jüergen Gall, Kurt Konolige, Helmut Gräbner and Charless C. Fowlkes and has published in prestigious journals such as Journal of the American Medical Informatics Association, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and DIAL (Catholic University of Leuven).

In The Last Decade

Xiaofeng Ren

9 papers receiving 767 citations

Hit Papers

RGB-(D) scene labeling: Features and algorithms 2012 2026 2016 2021 2012 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiaofeng Ren United States 8 668 167 117 86 69 9 790
David Hafner Germany 5 594 0.9× 142 0.9× 148 1.3× 46 0.5× 49 0.7× 11 712
Katrin Lasinger Austria 6 891 1.3× 229 1.4× 183 1.6× 46 0.5× 152 2.2× 8 1.1k
Martin Čadík Czechia 16 780 1.2× 135 0.8× 221 1.9× 62 0.7× 30 0.4× 36 978
Thanh-Toan Do Australia 19 608 0.9× 163 1.0× 65 0.6× 244 2.8× 58 0.8× 52 895
Maojun Zhang China 14 801 1.2× 181 1.1× 172 1.5× 102 1.2× 38 0.6× 123 964
Yiming Cui United States 14 547 0.8× 109 0.7× 65 0.6× 143 1.7× 31 0.4× 21 727
Asako Kanezaki Japan 14 537 0.8× 175 1.0× 52 0.4× 121 1.4× 144 2.1× 41 770
Yunhang Shen China 16 524 0.8× 64 0.4× 71 0.6× 248 2.9× 53 0.8× 48 725
El Mustapha Mouaddib France 13 542 0.8× 200 1.2× 115 1.0× 18 0.2× 140 2.0× 45 698
Seungkyu Lee South Korea 13 486 0.7× 114 0.7× 82 0.7× 26 0.3× 30 0.4× 63 687

Countries citing papers authored by Xiaofeng Ren

Since Specialization
Citations

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

Fields of papers citing papers by Xiaofeng Ren

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiaofeng Ren

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

All Works

9 of 9 papers shown
1.
Nie, Dong, Rui Lan, Ling Wang, & Xiaofeng Ren. (2022). Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 17263–17273. 28 indexed citations
2.
Si, Lu, Xiaofeng Ren, & Feng Liu. (2014). Depth Enhancement via Low-Rank Matrix Completion. 3390–3397. 121 indexed citations
3.
Ren, Xiaofeng, et al.. (2013). Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library. Journal of the American Medical Informatics Association. 20(6). 1076–1081. 5 indexed citations
4.
Ren, Xiaofeng & Deva Ramanan. (2013). Histograms of Sparse Codes for Object Detection. 3246–3253. 160 indexed citations
5.
Fossati, Andrea, Jüergen Gall, Helmut Gräbner, Xiaofeng Ren, & Kurt Konolige. (2012). Consumer Depth Cameras for Computer Vision: Research Topics and Applications. DIAL (Catholic University of Leuven). 27 indexed citations
6.
Jung, Jaeyeon, et al.. (2012). SensorSift. 149–158. 13 indexed citations
7.
Ren, Xiaofeng, Liefeng Bo, & D. Fox. (2012). RGB-(D) scene labeling: Features and algorithms. 2759–2766. 256 indexed citations breakdown →
8.
Fossati, Andrea, Jüergen Gall, Helmut Gräbner, Xiaofeng Ren, & Kurt Konolige. (2012). Consumer Depth Cameras for Computer Vision. 109 indexed citations
9.
Ren, Xiaofeng, Charless C. Fowlkes, & Jitendra Malik. (2005). Scale-invariant contour completion using conditional random fields. 1214–1221 Vol. 2. 71 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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