Inkyu Shin

34 total papers · 795 total citations
13 papers, 387 citations indexed

About

Inkyu Shin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Inkyu Shin has authored 13 papers receiving a total of 387 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 2 papers in Control and Systems Engineering. Recurrent topics in Inkyu Shin's work include Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (7 papers) and Human Pose and Action Recognition (4 papers). Inkyu Shin is often cited by papers focused on Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (7 papers) and Human Pose and Action Recognition (4 papers). Inkyu Shin collaborates with scholars based in South Korea, Canada and United States. Inkyu Shin's co-authors include In So Kweon, Seokju Lee, Fei Pan, François Rameau, Kuk‐Jin Yoon, Sanghyun Woo, Buyu Liu, Dong-Jin Kim, Yi‐Hsuan Tsai and Bingbing Zhuang and has published in prestigious journals such as Journal of Geochemical Exploration, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Inkyu Shin

13 papers receiving 383 citations

Hit Papers

Unsupervised Intra-Domain... 2020 2026 2022 2024 2020 50 100 150 200

Author Peers

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

Author Last Decade Papers Cites
Inkyu Shin 296 243 65 37 31 13 387
Fabio Maria Carlucci 291 1.0× 315 1.3× 72 1.1× 21 0.6× 11 0.4× 11 405
Chen Liu 231 0.8× 317 1.3× 69 1.1× 34 0.9× 16 0.5× 13 439
Zongyan Han 171 0.6× 250 1.0× 74 1.1× 16 0.4× 14 0.5× 13 369
Ruijia Xu 175 0.6× 224 0.9× 23 0.4× 22 0.6× 28 0.9× 9 348
Rafael de Oliveira Werneck 125 0.4× 189 0.8× 35 0.5× 47 1.3× 25 0.8× 15 405
Qianfen Jiao 293 1.0× 307 1.3× 53 0.8× 18 0.5× 10 0.3× 12 431
Guangyao Chen 136 0.5× 231 1.0× 24 0.4× 32 0.9× 26 0.8× 10 377
Xiangru Lin 344 1.2× 194 0.8× 25 0.4× 45 1.2× 13 0.4× 15 441
Marco Toldo 232 0.8× 247 1.0× 65 1.0× 17 0.5× 9 0.3× 12 365
Pedro Ribeiro Mendes Júnior 102 0.3× 178 0.7× 35 0.5× 26 0.7× 32 1.0× 18 350

Countries citing papers authored by Inkyu Shin

Since Specialization
Citations

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

Fields of papers citing papers by Inkyu Shin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Inkyu Shin

This figure shows the co-authorship network connecting the top 25 collaborators of Inkyu Shin. A scholar is included among the top collaborators of Inkyu Shin 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 Inkyu Shin. Inkyu Shin 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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