Jungbeom Lee

585 total citations
6 papers, 209 citations indexed

About

Jungbeom Lee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Conservation. According to data from OpenAlex, Jungbeom Lee has authored 6 papers receiving a total of 209 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Conservation. Recurrent topics in Jungbeom Lee's work include Advanced Image and Video Retrieval Techniques (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Jungbeom Lee is often cited by papers focused on Advanced Image and Video Retrieval Techniques (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Domain Adaptation and Few-Shot Learning (2 papers). Jungbeom Lee collaborates with scholars based in South Korea, United States and Germany. Jungbeom Lee's co-authors include Sungroh Yoon, Hyunwoo Kim, Eunji Kim, Sungwon Kim, Jooyoung Choi, Sangdoo Yun, Seong Joon Oh, Junsuk Choe, Siwon Kim and Jaeyoung Do and has published in prestigious journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and British Machine Vision Conference.

In The Last Decade

Jungbeom Lee

6 papers receiving 204 citations

Peers

Jungbeom Lee
Yuanfeng Ji China
Vighnesh Birodkar United States
Bingchen Liu Netherlands
Ya-Liang Chang Taiwan
Utkarsh Ojha United States
Jiefeng Peng China
Zelin Peng China
Chuanxia Zheng Singapore
Yiwu Zhong United States
Yuanfeng Ji China
Jungbeom Lee
Citations per year, relative to Jungbeom Lee Jungbeom Lee (= 1×) peers Yuanfeng Ji

Countries citing papers authored by Jungbeom Lee

Since Specialization
Citations

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

Fields of papers citing papers by Jungbeom Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jungbeom Lee

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

All Works

6 of 6 papers shown
1.
Lee, Jungbeom, et al.. (2023). Weakly Supervised Referring Image Segmentation with Intra-Chunk and Inter-Chunk Consistency. 21813–21824. 4 indexed citations
2.
Choi, Jooyoung, et al.. (2022). Perception Prioritized Training of Diffusion Models. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11462–11471. 99 indexed citations
3.
Kim, Eunji, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, & Sungroh Yoon. (2022). Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 14238–14247. 32 indexed citations
4.
Lee, Jungbeom, Seong Joon Oh, Sangdoo Yun, et al.. (2022). Weakly Supervised Semantic Segmentation using Out-of-Distribution Data. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16876–16885. 70 indexed citations
5.
Lee, Jungbeom, Jangho Lee, Sungmin Lee, & Sungroh Yoon. (2018). Mutual Suppression Network for Video Prediction using Disentangled Features.. British Machine Vision Conference. 296. 1 indexed citations
6.
Kim, Yoojin, et al.. (2008). A study on infrared reflectography for underdrawing detection using a digital camera. 128–134. 3 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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2026