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Researcher · Naver (South Korea) · South Korea

Junbum Cha

Junbum Cha publishes mostly in Computer Vision and Pattern Recognition, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, on topics such as Multimodal Machine Learning Applications, Domain Adaptation and Few-Shot Learning and Video Analysis and Summarization.

ORCID 0000-0001-8248-3138 OpenAlex
World rank
#1,457,788
of 7,710,434 ranked researchers
Rank in South Korea
#16,285
of 162,609
Works
22
Citations
628
Citations per work
28.5
h-index
9
i10-index 8Top 52% in Computer Vision and Pattern Recognition
Junbum Cha

What does Junbum Cha research?

Junbum Cha's work is mostly in Computer Science (86%), Biochemistry, Genetics and Molecular Biology (6%), Medicine (3%).

Physical Sciences: 19 works · 86.0% of their workComputer Science: 19 works · 86.0% of their workComputer Vision and Pattern Recognition: 11 works · 51.6% of their workArtificial Intelligence: 7 works · 32.8% of their workComputer Graphics and Computer-Aided Design: 0 works · 1.6% of their workLife Sciences: 1 works · 6.2% of their workBiochemistry, Genetics and Molecular Biology: 1 works · 6.2% of their workCancer Research: 1 works · 3.1% of their workMolecular Biology: 1 works · 3.1% of their workEverything else: 1 works · 4.7% of their workHealth Sciences: 1 works · 3.1% of their workMedicine: 1 works · 3.1% of their workRadiology, Nuclear Medicine and Imaging: 1 works · 3.1% of their workTHEIR WORKJunbum Cha22works
Life Sciences6%Physical Sciences86%Health Sciences3%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science1986.0%
    1. Biochemistry, Genetics and Molecular Biology16.2%
  3. Everything else14.7%
    1. Medicine13.1%
  1. 1Computer Vision and Pattern Recognition52% of their works
  2. 2Artificial Intelligence33% of their works
  3. 3Radiology, Nuclear Medicine and Imaging3% of their works
  4. 4Molecular Biology3% of their works
  5. 5Cancer Research3% of their works
  6. 6Computer Graphics and Computer-Aided Design2% of their works

Research topics

Which keywords describe Junbum Cha's research?

The keywords of their largest research topics: Neural Networks, Deep Learning, Representation Learning, Unsupervised Learning, Image Captioning, Visual Question Answering, Domain Adaptation and Transfer Learning.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 20 words, with their numbers
  1. Neural Networks244 topics
  2. Deep Learning162 topics
  3. Representation Learning152 topics
  4. Unsupervised Learning152 topics
  5. Image Captioning101 topic
  6. Visual Question Answering101 topic
  7. Domain Adaptation91 topic
  8. Transfer Learning91 topic
  9. Content-Based Retrieval71 topic
  10. Video Summarization71 topic
  11. Generative Adversarial Networks61 topic
  12. Image Synthesis61 topic
  13. Machine Translation51 topic
  14. Word Representation51 topic
  15. Handwriting Recognition31 topic
  16. Neural Machine Translation31 topic
  17. Statistical Machine Translation31 topic
  18. Text Detection31 topic
  19. Cancer21 topic
  20. Long Noncoding RNAs21 topic

Where does Junbum Cha work?

Junbum Cha's main affiliation in the publication record is Naver (South Korea), South Korea.

How many publications and citations does Junbum Cha have?

OpenAlex counts 22 works and 628 citations for Junbum Cha, an h-index of 9, ranking #1,457,788 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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