Yumin Suh

694 total citations
13 papers, 196 citations indexed

About

Yumin Suh is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Yumin Suh has authored 13 papers receiving a total of 196 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 2 papers in Computer Networks and Communications. Recurrent topics in Yumin Suh's work include Domain Adaptation and Few-Shot Learning (7 papers), Multimodal Machine Learning Applications (6 papers) and Advanced Neural Network Applications (5 papers). Yumin Suh is often cited by papers focused on Domain Adaptation and Few-Shot Learning (7 papers), Multimodal Machine Learning Applications (6 papers) and Advanced Neural Network Applications (5 papers). Yumin Suh collaborates with scholars based in United States, South Korea and Netherlands. Yumin Suh's co-authors include Kyoung Mu Lee, Wonsik Kim, Bohyung Han, Samuel Schulter, Manmohan Chandraker, Masoud Faraki, Amit K. Roy–Chowdhury, Christian Simon, Yi‐Hsuan Tsai and Mehrtash Harandi and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

In The Last Decade

Yumin Suh

12 papers receiving 191 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yumin Suh United States 6 142 110 25 17 9 13 196
Yaohui Cai United States 4 162 1.1× 117 1.1× 10 0.4× 16 0.9× 17 1.9× 5 235
Yury Nahshan Israel 3 185 1.3× 127 1.2× 14 0.6× 9 0.5× 17 1.9× 3 235
Ben Harwood Australia 5 244 1.7× 137 1.2× 53 2.1× 38 2.2× 17 1.9× 6 306
Adel Bibi Saudi Arabia 8 233 1.6× 78 0.7× 11 0.4× 45 2.6× 11 1.2× 20 300
Jiajia Wu China 9 79 0.6× 42 0.4× 12 0.5× 15 0.9× 4 0.4× 18 142
Zheng Zhan United States 7 83 0.6× 66 0.6× 18 0.7× 13 0.8× 28 3.1× 22 167
Apoorv Vyas Switzerland 4 87 0.6× 82 0.7× 35 1.4× 15 0.9× 10 1.1× 7 182

Countries citing papers authored by Yumin Suh

Since Specialization
Citations

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

Fields of papers citing papers by Yumin Suh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yumin Suh

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

All Works

13 of 13 papers shown
1.
2.
Zhao, Shiyu, L. Zhao, Vijay Kumar B G, et al.. (2024). Generating Enhanced Negatives for Training Language-Based Object Detectors. 13592–13602. 3 indexed citations
3.
Zhao, Shiyu, Samuel Schulter, L. Zhao, et al.. (2024). Taming Self-Training for Open-Vocabulary Object Detection. 13938–13947. 5 indexed citations
4.
Schulter, Samuel, et al.. (2023). Efficient Controllable Multi-Task Architectures. 5717–5728. 4 indexed citations
5.
Deng, Weijian, et al.. (2023). Split to Learn: Gradient Split for Multi-Task Human Image Analysis. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 4340–4349. 1 indexed citations
6.
Schulter, Samuel, et al.. (2023). OmniLabel: A Challenging Benchmark for Language-Based Object Detection. 11919–11928. 4 indexed citations
7.
Simon, Christian, Masoud Faraki, Yi‐Hsuan Tsai, et al.. (2022). On Generalizing Beyond Domains in Cross-Domain Continual Learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9255–9264. 20 indexed citations
8.
Suh, Yumin, et al.. (2022). Controllable Dynamic Multi-Task Architectures. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10945–10954. 19 indexed citations
9.
Seo, Seonguk, Yumin Suh, Dongwan Kim, Jong‐Woo Han, & Bohyung Han. (2019). Learning to Optimize Domain Specific Normalization with Domain Augmentation for Domain Generalization. arXiv (Cornell University). 1 indexed citations
10.
Suh, Yumin, Bohyung Han, Wonsik Kim, & Kyoung Mu Lee. (2019). Stochastic Class-Based Hard Example Mining for Deep Metric Learning. 7244–7252. 81 indexed citations
11.
Suh, Yumin, et al.. (2015). Subgraph matching using compactness prior for robust feature correspondence. 5070–5078. 18 indexed citations
12.
Suh, Yumin, et al.. (2015). Discrete Tabu Search for Graph Matching. 109–117. 36 indexed citations
13.
Suh, Yumin, et al.. (1984). Morphology and reproduction of some species of Ceramium (Rhodophyta) in culture. 4 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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