Junbum Cha

517 total citations
8 papers, 157 citations indexed

About

Junbum Cha is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Junbum Cha has authored 8 papers receiving a total of 157 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Molecular Biology. Recurrent topics in Junbum Cha's work include Topic Modeling (3 papers), Advanced Image and Video Retrieval Techniques (2 papers) and Advanced Text Analysis Techniques (2 papers). Junbum Cha is often cited by papers focused on Topic Modeling (3 papers), Advanced Image and Video Retrieval Techniques (2 papers) and Advanced Text Analysis Techniques (2 papers). Junbum Cha collaborates with scholars based in South Korea, Germany and Japan. Junbum Cha's co-authors include Hyunjung Shim, Sanghyuk Chun, Byungseok Roh, Jonghwan Mun, Bado Lee, Sanghyun Park, Junyeop Lee, Sungrae Park, Ji‐Hoon Kim and Hwalsuk Lee and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and Proceedings of the AAAI Conference on Artificial Intelligence.

In The Last Decade

Junbum Cha

7 papers receiving 151 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Junbum Cha South Korea	5	114	47	43	14	10	8	157
Tongzheng Ren United States	4	72 0.6×	18 0.4×	54 1.3×	8 0.6×	7 0.7×	10	121
Parmida Atighehchian Canada	2	114 1.0×	29 0.6×	43 1.0×	20 1.4×	4 0.4×	2	182
Haibo Chen China	5	171 1.5×	31 0.7×	22 0.5×	9 0.6×	4 0.4×	13	208
Jonas Geiping United States	4	85 0.7×	17 0.4×	54 1.3×	3 0.2×	4 0.4×	16	148
Yasumasa Onoe United States	7	91 0.8×	12 0.3×	128 3.0×	3 0.2×	17 1.7×	12	202
Guojun Yin China	5	129 1.1×	7 0.1×	31 0.7×	5 0.4×	2 0.2×	8	140
Sjoerd van Steenkiste Switzerland	6	69 0.6×	5 0.1×	56 1.3×	7 0.5×	2 0.2×	14	117
Guangxuan Xiao United States	3	42 0.4×	7 0.1×	56 1.3×	6 0.4×	7 0.7×	4	112
Francis Dutil Canada	5	71 0.6×	3 0.1×	83 1.9×	5 0.4×	3 0.3×	9	125
Mingyang Ling China	4	186 1.6×	3 0.1×	116 2.7×	4 0.3×	6 0.6×	6	212

Countries citing papers authored by Junbum Cha

Since Specialization

Citations

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

Fields of papers citing papers by Junbum Cha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junbum Cha

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

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Cha, Junbum, et al.. (2024). Honeybee: Locality-Enhanced Projector for Multimodal LLM. 13817–13827. 19 indexed citations

2.

Cha, Junbum, Jonghwan Mun, & Byungseok Roh. (2023). Learning to Generate Text-Grounded Mask for Open-World Semantic Segmentation from Only Image-Text Pairs. 11165–11174. 33 indexed citations

3.

Chun, Sanghyuk, et al.. (2022). Few-Shot Font Generation With Weakly Supervised Localized Representations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(3). 1479–1495. 11 indexed citations

4.

Chun, Sanghyuk, et al.. (2021). Multiple Heads are Better than One: Few-shot Font Generation with Multiple Localized Experts. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 13880–13889. 41 indexed citations

5.

Chun, Sanghyuk, et al.. (2021). Few-shot Font Generation with Localized Style Representations and Factorization. Proceedings of the AAAI Conference on Artificial Intelligence. 35(3). 2393–2402. 49 indexed citations

6.

Park, Sungrae, Geewook Kim, Junyeop Lee, et al.. (2020). Scale down Transformer by Grouping Features for a Lightweight Character-level Language Model. 6883–6893.

7.

Cha, Junbum, et al.. (2016). A method for obtaining rich data from PubMed using SVM. 37–39. 1 indexed citations

8.

Cha, Junbum, et al.. (2016). GRiD: Gathering rich data from PubMed using one-class SVM. 12. 4325–4331. 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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