Mingi Ji

593 citations
11 papers · 334 · h-index 7

Impact in

    • Advanced Neural Network Applications
    • Multimodal Machine Learning Applications
    • Advanced Image and Video Retrieval Techniques
    • Handwritten Text Recognition Techniques
    • Domain Adaptation and Few-Shot Learning
    • Topic Modeling
    • Natural Language Processing Techniques

Papers in

    • Topic Modeling 3
    • Domain Adaptation and Few-Shot Learning 3
    • Adversarial Robustness in Machine Learning 2
    • Semantic Web and Ontologies 1
    • Multimodal Machine Learning Applications 2
    • Handwritten Text Recognition Techniques 2
    • Advanced Neural Network Applications 2
Journals
Proceedings of the AAAI Conference on Artificial Intelligence (5 papers)Journal of Korean Institute of Industrial Engineers (1 paper)

In The Last Decade

Mingi Ji

10 papers receiving 325 citations

Peers

Mingi Ji
Comparison fields: 5 of 71
  • Computer Vision and Pattern Recognition 188
  • Artificial Intelligence 189
  • Signal Processing 25
  • Information Systems 49
  • Media Technology 19
Replace Jian Cao with:
Jian Cao China
Rong Xiao China
Litao Yu Australia
Lingyun Song China
Mohammed Falah Mohammed Malaysia
Chandan Gautam India
Meiyu Liang China
Nabiha Azizi Algeria
Haoli Bai China
Baoyun Peng China
Mingi Ji relative to Jian Cao China Jian Cao's profile →
Citations per field
00.5×1.5×1.8×
Jian Cao · 1×
Citations per year

Countries citing papers authored by Mingi Ji

Since Specialization
Citations

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

Fields of papers citing papers by Mingi Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 11 scholars most cited alongside Mingi Ji, 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 Mingi Ji Line = papers co-authored together Mingi Ji links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1 2021113
2 202193
3 202258
4 201923
5 202021
6
BROS: A Pre-trained Language Model for Understanding Texts in Document
202115
7 20187
8 20192
9 20221
10 20231
11 20240

About Mingi Ji

Mingi Ji is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Civil and Structural Engineering and Control and Systems Engineering, having authored 11 papers that have together received 334 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (3 papers), Topic Modeling (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Multimodal Machine Learning Applications (2 papers), Handwritten Text Recognition Techniques (2 papers), Adversarial Robustness in Machine Learning (2 papers), Advanced Neural Network Applications (2 papers) and Semantic Web and Ontologies (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (188 citations), Artificial Intelligence (189 citations), Signal Processing (25 citations), Information Systems (49 citations) and Media Technology (19 citations). Mingi Ji has collaborated with scholars based in South Korea, Canada and United States. Frequent co-authors include Il‐Chul Moon, Byeongho Heo, Seungjae Shin, Gibeom Park, Teakgyu Hong, Dae‐Hyun Nam, Wonseok Hwang, Kyungwoo Song, Jinkyoo Park and Seung-won Hwang. Their work appears in journals such as Proceedings of the AAAI Conference on Artificial Intelligence and Journal of Korean Institute of Industrial Engineers.

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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