Taoran Yi

14 total papers · 790 total citations
3 papers, 190 citations indexed

About

Taoran Yi is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Signal Processing. According to data from OpenAlex, Taoran Yi has authored 3 papers receiving a total of 190 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Computer Vision and Pattern Recognition, 3 papers in Computer Graphics and Computer-Aided Design and 1 paper in Signal Processing. Recurrent topics in Taoran Yi's work include Computer Graphics and Visualization Techniques (3 papers), Advanced Vision and Imaging (2 papers) and Video Analysis and Summarization (1 paper). Taoran Yi is often cited by papers focused on Computer Graphics and Visualization Techniques (3 papers), Advanced Vision and Imaging (2 papers) and Video Analysis and Summarization (1 paper). Taoran Yi collaborates with scholars based in China. Taoran Yi's co-authors include Jiemin Fang, Xinggang Wang, Wenyu Liu, Guanjun Wu, Lingxi Xie, Qi Tian, Wei Wei, Xiaopeng Zhang and Xiaopeng Zhang and has published in prestigious journals such as .

In The Last Decade

Taoran Yi

3 papers receiving 183 citations

Hit Papers

4D Gaussian Splatting for... 2024 2026 2024 50 100 150

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Taoran Yi 132 69 57 25 14 3 190
Guanjun Wu 134 1.0× 69 1.0× 57 1.0× 25 1.0× 17 1.2× 8 250
Oscar Michel 149 1.1× 95 1.4× 96 1.7× 19 0.8× 30 2.1× 2 225
Matt Deitke 153 1.2× 95 1.4× 96 1.7× 20 0.8× 31 2.2× 4 231
Jhony Kaesemodel Pontes 187 1.4× 82 1.2× 102 1.8× 28 1.1× 11 0.8× 6 255
Eli VanderBilt 161 1.2× 96 1.4× 98 1.7× 23 0.9× 35 2.5× 3 245
Anpei Chen 196 1.5× 125 1.8× 83 1.5× 35 1.4× 7 0.5× 5 256
Chiyu Max Jiang 99 0.8× 48 0.7× 53 0.9× 9 0.4× 20 1.4× 5 178
Yawar Siddiqui 155 1.2× 128 1.9× 126 2.2× 16 0.6× 11 0.8× 8 226
Daeyun Shin 127 1.0× 46 0.7× 87 1.5× 33 1.3× 6 0.4× 5 166
Yuval Bahat 196 1.5× 43 0.6× 49 0.9× 10 0.4× 6 0.4× 7 245

Countries citing papers authored by Taoran Yi

Since Specialization
Citations

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

Fields of papers citing papers by Taoran Yi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Taoran Yi

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

All Works

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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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