Sangwon Kim

619 total citations · 1 hit paper
26 papers, 398 citations indexed

About

Sangwon Kim is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Sangwon Kim has authored 26 papers receiving a total of 398 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 5 papers in Signal Processing. Recurrent topics in Sangwon Kim's work include Human Pose and Action Recognition (4 papers), Video Surveillance and Tracking Methods (4 papers) and Advanced Vision and Imaging (3 papers). Sangwon Kim is often cited by papers focused on Human Pose and Action Recognition (4 papers), Video Surveillance and Tracking Methods (4 papers) and Advanced Vision and Imaging (3 papers). Sangwon Kim collaborates with scholars based in South Korea and Japan. Sangwon Kim's co-authors include Byoung Chul Ko, Jae-Yeal Nam, Chang-Hyun Park, Jaegwan Shin, Jinwoo Kwak, Zeeshan Haider Jaffari, Kangmin Chon, Ather Abbas, Kyung Hwa Cho and Changgil Son and has published in prestigious journals such as Journal of Hazardous Materials, IEEE Access and Sensors.

In The Last Decade

Sangwon Kim

26 papers receiving 386 citations

Hit Papers

STAR-Transformer: A Spati... 2023 2026 2024 2023 25 50 75 100
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.

  1. STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition
    2023 112 citations Sangwon Kim, Byoung Chul Ko et al. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sangwon Kim South Korea 11 196 111 82 57 50 26 398
B. Perumal India 10 98 0.5× 62 0.6× 28 0.3× 69 1.2× 42 0.8× 50 247
Jiandan Zhong China 5 281 1.4× 130 1.2× 61 0.7× 10 0.2× 30 0.6× 8 440
Manik Gupta India 9 44 0.2× 45 0.4× 17 0.2× 72 1.3× 54 1.1× 47 256
Muhammad Irsyad Abdullah Malaysia 11 51 0.3× 35 0.3× 26 0.3× 45 0.8× 64 1.3× 44 303
Tin Lai Australia 11 143 0.7× 73 0.7× 18 0.2× 32 0.6× 17 0.3× 26 305
Vitaly Ablavsky United States 13 351 1.8× 101 0.9× 16 0.2× 44 0.8× 11 0.2× 28 433
Biyi Fang United States 7 122 0.6× 40 0.4× 44 0.5× 86 1.5× 124 2.5× 7 292
Sanaa El Fkihi Morocco 9 96 0.5× 53 0.5× 25 0.3× 61 1.1× 41 0.8× 52 279
Junfei Qiao China 13 55 0.3× 236 2.1× 27 0.3× 23 0.4× 138 2.8× 34 550
Shuting He China 14 313 1.6× 104 0.9× 39 0.5× 9 0.2× 31 0.6× 39 509

Countries citing papers authored by Sangwon Kim

Since Specialization
Citations

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

Fields of papers citing papers by Sangwon Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sangwon Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Sangwon Kim. A scholar is included among the top collaborators of Sangwon 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 Sangwon Kim. Sangwon Kim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kim, Sangwon, et al.. (2024). Domain-free fire detection using the spatial–temporal attention transform of the YOLO backbone. Pattern Analysis and Applications. 27(2). 14 indexed citations
2.
Jaffari, Zeeshan Haider, Ather Abbas, Chang‐Min Kim, et al.. (2023). Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents. Journal of Hazardous Materials. 462. 132773–132773. 67 indexed citations
3.
Kim, Sangwon, et al.. (2023). Cross-Modal Learning with 3D Deformable Attention for Action Recognition. 10231–10241. 21 indexed citations
4.
Kim, Sangwon, et al.. (2023). STAR-Transformer: A Spatio-temporal Cross Attention Transformer for Human Action Recognition. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3319–3328. 112 indexed citations breakdown →
5.
Kim, Sangwon, et al.. (2023). STAR++: Rethinking spatio-temporal cross attention transformer for video action recognition. Applied Intelligence. 53(23). 28446–28459. 5 indexed citations
6.
Kim, Sangwon & Byoung Chul Ko. (2023). Neural Tree Decoder for Interpretation of Vision Transformers. IEEE Transactions on Artificial Intelligence. 5(5). 2067–2078. 3 indexed citations
7.
Kim, Sangwon, et al.. (2022). SSL-MOT: self-supervised learning based multi-object tracking. Applied Intelligence. 53(1). 930–940. 7 indexed citations
8.
Kim, Sangwon, et al.. (2022). Sumav: Fully automated malware labeling. ICT Express. 8(4). 530–538. 3 indexed citations
9.
Kim, Sangwon, et al.. (2022). Shift- ViT: Siamese Vision Transformer using Shifted Branches. 259–261. 2 indexed citations
10.
Kim, Sangwon, Byoung Chul Ko, & Jae-Yeal Nam. (2021). Model Simplification of Deep Random Forest for Real-Time Applications of Various Sensor Data. Sensors. 21(9). 3004–3004. 5 indexed citations
11.
Kim, Sangwon, et al.. (2021). Lightweight surrogate random forest support for model simplification and feature relevance. Applied Intelligence. 52(1). 471–481. 12 indexed citations
12.
Kim, Sangwon, et al.. (2021). Self-Supervised Keypoint Detection Based on Multi-Layer Random Forest Regressor. IEEE Access. 9. 40850–40859. 10 indexed citations
13.
Kim, Sangwon, et al.. (2020). Energy Efficient Pupil Tracking Based on Rule Distillation of Cascade Regression Forest. Sensors. 20(18). 5141–5141. 11 indexed citations
14.
Kim, Sangwon, et al.. (2020). Evaluation of Image Similarity Algorithms for Malware Fake-Icon Detection. 1638–1640. 2 indexed citations
15.
Kim, Sangwon & Byoung Chul Ko. (2020). Building Deep Random Ferns Without Backpropagation. IEEE Access. 8. 8533–8542. 10 indexed citations
16.
Kim, Sangwon, et al.. (2020). Online Multiple Object Tracking Using Rule Distillated Siamese Random Forest. IEEE Access. 8. 182828–182841. 12 indexed citations
17.
Kim, Sangwon, et al.. (2019). Deep Coupling of Random Ferns.. Computer Vision and Pattern Recognition. 5–8. 2 indexed citations
18.
Kim, Sangwon, Jae-Yeal Nam, & Byoung Chul Ko. (2019). Fast Depth Estimation in a Single Image Using Lightweight Efficient Neural Network. Sensors. 19(20). 4434–4434. 4 indexed citations
19.
Kim, Sangwon, et al.. (2019). Android Malware Similarity Clustering using Method based Opcode Sequence and Jaccard Index. 178–183. 7 indexed citations
20.
Seo, Euiseong, et al.. (2010). Dynamic alteration schemes of real-time schedules for I/O device energy efficiency. ACM Transactions on Embedded Computing Systems. 10(2). 1–32. 2 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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