Jun-Hwa Kim

480 total citations · 1 hit paper
10 papers, 279 citations indexed

About

Jun-Hwa Kim is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Jun-Hwa Kim has authored 10 papers receiving a total of 279 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 1 paper in Signal Processing. Recurrent topics in Jun-Hwa Kim's work include Anomaly Detection Techniques and Applications (4 papers), Video Surveillance and Tracking Methods (3 papers) and Human Pose and Action Recognition (3 papers). Jun-Hwa Kim is often cited by papers focused on Anomaly Detection Techniques and Applications (4 papers), Video Surveillance and Tracking Methods (3 papers) and Human Pose and Action Recognition (3 papers). Jun-Hwa Kim collaborates with scholars based in South Korea. Jun-Hwa Kim's co-authors include Chee Sun Won and Nam‐Ho Kim and has published in prestigious journals such as IEEE Access, Applied Sciences and Engineering Applications of Artificial Intelligence.

In The Last Decade

Jun-Hwa Kim

10 papers receiving 273 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset

2022 146 citations Jun-Hwa Kim, Nam‐Ho Kim et al. Journal of Marine Science and Engineering profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jun-Hwa Kim South Korea	6	151	45	42	33	27	10	279
Zhuofan Zong China	3	192 1.3×	51 1.1×	68 1.6×	20 0.6×	24 0.9×	5	294
Xinming Guo China	4	129 0.9×	30 0.7×	20 0.5×	19 0.6×	42 1.6×	6	279
Ch. Venkata Rami Reddy India	3	108 0.7×	26 0.6×	17 0.4×	17 0.5×	40 1.5×	7	254
Brett Koonce	2	104 0.7×	21 0.5×	64 1.5×	21 0.6×	15 0.6×	2	267
Bharat Mahaur India	6	177 1.2×	48 1.1×	31 0.7×	23 0.7×	40 1.5×	9	271
Lixia Deng China	8	191 1.3×	104 2.3×	36 0.9×	10 0.3×	40 1.5×	28	342
Yuanduo Hong China	4	144 1.0×	21 0.5×	54 1.3×	25 0.8×	27 1.0×	5	262
Kin Wai Lau Hong Kong	5	184 1.2×	34 0.8×	19 0.5×	16 0.5×	53 2.0×	8	308
Jeonghyun Baek South Korea	9	193 1.3×	35 0.8×	44 1.0×	11 0.3×	16 0.6×	32	316

Countries citing papers authored by Jun-Hwa Kim

Since Specialization

Citations

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

Fields of papers citing papers by Jun-Hwa Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun-Hwa Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Jun-Hwa Kim. A scholar is included among the top collaborators of Jun-Hwa Kim 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 Jun-Hwa Kim. Jun-Hwa Kim 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:

10 of 10 papers shown

1.

Kim, Jun-Hwa, Nam‐Ho Kim, & Chee Sun Won. (2024). Global–local feature learning for fine-grained food classification based on Swin Transformer. Engineering Applications of Artificial Intelligence. 133. 108248–108248. 18 indexed citations

2.

Kim, Jun-Hwa & Chee Sun Won. (2024). Audio-Visual Action Recognition Using Transformer Fusion Network. Applied Sciences. 14(3). 1190–1190. 1 indexed citations

3.

Kim, Jun-Hwa, et al.. (2024). Multimodal Food Image Classification with Large Language Models. Electronics. 13(22). 4552–4552. 4 indexed citations

4.

Kim, Jun-Hwa, et al.. (2024). Advanced Facial Analysis in Multi-Modal Data with Cascaded Cross-Attention based Transformer. 7870–7877. 1 indexed citations

5.

Kim, Jun-Hwa, Nam‐Ho Kim, & Chee Sun Won. (2023). High-Speed Drone Detection Based On Yolo-V8. 1–2. 65 indexed citations

6.

Kim, Jun-Hwa, et al.. (2022). Object Detection and Classification Based on YOLO-V5 with Improved Maritime Dataset. Journal of Marine Science and Engineering. 10(3). 377–377. 146 indexed citations breakdown →

7.

Kim, Nam‐Ho, Jun-Hwa Kim, & Chee Sun Won. (2022). FAFD: Fast and Accurate Face Detector. Electronics. 11(6). 875–875. 6 indexed citations

8.

Kim, Jun-Hwa, Nam‐Ho Kim, & Chee Sun Won. (2021). Deep Edge Computing for Videos. IEEE Access. 9. 123348–123357. 11 indexed citations

9.

Kim, Jun-Hwa & Chee Sun Won. (2020). Emotion Enhancement for Facial Images Using GAN. 1–4. 3 indexed citations

10.

Kim, Jun-Hwa & Chee Sun Won. (2020). Action Recognition in Videos Using Pre-Trained 2D Convolutional Neural Networks. IEEE Access. 8. 60179–60188. 24 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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