Jonghwan Mun

1.3k citations
16 papers · 652 · h-index 12

Impact in

Papers in

Jonghwan Mun

16 papers receiving 635 citations

Peers

Jonghwan Mun
Comparison fields: 5 of 73
  • Computer Vision and Pattern Recognition 442
  • Pharmaceutical Science 50
  • Artificial Intelligence 202
  • Public Health, Environmental and Occupational Health 80
  • Ophthalmology 20
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Hao Fang China
Jingyu Shao Australia
Preeti Sharma India
Shijia Liu China
Jia‐Hong Lee Taiwan
Yassine Marrakchi Spain
Ali Hojjatoleslami United Kingdom
Caiyong Wang China
Ricardo Santos Portugal
Feng‐Yu Chang Taiwan
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Citations per year

Countries citing papers authored by Jonghwan Mun

Since Specialization
Citations

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

Fields of papers citing papers by Jonghwan Mun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jonghwan Mun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jonghwan Mun Line = papers co-authored together Jonghwan Mun links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2020179
2 201994
3 201965
4 201754
5 202246
6 202344
7
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization
201734
8 202333
9 202026
10 202419
11
Learning to Specialize with Knowledge Distillation for Visual Question Answering
201816
12 202215
13 20197
14 20237
15 20237
16 20216

About Jonghwan Mun

Jonghwan Mun is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Public Health, Environmental and Occupational Health, Radiology, Nuclear Medicine and Imaging and Pharmaceutical Science, having authored 16 papers that have together received 652 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (10 papers), Advanced Image and Video Retrieval Techniques (5 papers), Domain Adaptation and Few-Shot Learning (5 papers), Human Pose and Action Recognition (4 papers), Ocular Surface and Contact Lens (3 papers), Corneal Surgery and Treatments (3 papers), Advanced Neural Network Applications (3 papers) and Advanced Drug Delivery Systems (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (442 citations), Pharmaceutical Science (50 citations), Artificial Intelligence (202 citations), Public Health, Environmental and Occupational Health (80 citations) and Ophthalmology (20 citations). Jonghwan Mun has collaborated with scholars based in South Korea, Germany and Japan. Frequent co-authors include Bohyung Han, Minsu Cho, Sei Kwang Hahn, Zhou Ren, Linjie Yang, Ning Xu, Byungseok Roh, Junbum Cha, Choun‐Ki Joo and Jee Won Mok. Their work appears in journals such as RSC Advances, Biomaterials Science, Advanced Drug Delivery Reviews, Seoul National University Open Repository (Seoul National University) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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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