Junae Kim

530 citations
14 papers · 326 · h-index 9

Impact in

    • Face and Expression Recognition
    • Video Surveillance and Tracking Methods
    • Advanced Image and Video Retrieval Techniques
    • Face recognition and analysis
    • Human Pose and Action Recognition
    • Image Retrieval and Classification Techniques
    • Domain Adaptation and Few-Shot Learning

Papers in

Junae Kim

13 papers receiving 316 citations

Peers

Junae Kim
Comparison fields: 5 of 49
  • Computer Vision and Pattern Recognition 236
  • Artificial Intelligence 118
  • Signal Processing 30
  • Media Technology 24
  • Computational Mechanics 42
Replace Victor Lempitsky with:
Victor Lempitsky Russia
N.B. Karayiannis United States
Matthieu Courbariaux France
Murat H. Sazlı Türkiye
Chunhua Shen Australia
Lionel Lacassagne France
Douglas R. Heisterkamp United States
Shibin Parameswaran United States
Minzhe Li China
Vassilis Kalofolias Switzerland
Junae Kim relative to Victor Lempitsky Russia Victor Lempitsky's profile →
Citations per field
00.5×5.5×
Victor Lempitsky · 1×
Citations per year

Countries citing papers authored by Junae Kim

Since Specialization
Citations

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

Fields of papers citing papers by Junae Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 16 scholars most cited alongside Junae Kim, 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 Junae Kim Line = papers co-authored together Junae Kim links everyone, so they are left out of the graph.

All Works

14 of 14 papers shown
#Work
1 200960
2 200958
3 201251
4 201049
5 201430
6 201117
7 201116
8 202215
9 20178
10 20127
11 20127
12 20247
13 20221
14 20240

About Junae Kim

Junae Kim is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics, Signal Processing and Computer Networks and Communications, having authored 14 papers that have together received 326 indexed citations. Recurring topics across this work include Face and Expression Recognition (6 papers), Sparse and Compressive Sensing Techniques (6 papers), Advanced Malware Detection Techniques (3 papers), Advanced Image and Video Retrieval Techniques (3 papers), Machine Learning and Algorithms (3 papers), Neuroscience and Neural Engineering (2 papers), Network Security and Intrusion Detection (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (236 citations), Artificial Intelligence (118 citations), Signal Processing (30 citations), Media Technology (24 citations) and Computational Mechanics (42 citations). Junae Kim has collaborated with scholars based in Australia, Switzerland and Italy. Frequent co-authors include Lei Wang, Chunhua Shen, Anton van den Hengel, Hanzi Wang, Chunhua Shen, Chunhua Shen, Fayao Liu, Paul Montague, Xuming He and Chin‐Teng Lin. Their work appears in journals such as IEEE Transactions on Mobile Computing, Applied Sciences, ACM Computing Surveys, Journal of Machine Learning Research and IEEE Transactions on Circuits and Systems for Video Technology.

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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