Sungmin Cha

684 total citations
12 papers, 238 citations indexed

About

Sungmin Cha is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Sungmin Cha has authored 12 papers receiving a total of 238 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 2 papers in Media Technology. Recurrent topics in Sungmin Cha's work include Advanced Image Processing Techniques (3 papers), Multimodal Machine Learning Applications (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Sungmin Cha is often cited by papers focused on Advanced Image Processing Techniques (3 papers), Multimodal Machine Learning Applications (3 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Sungmin Cha collaborates with scholars based in South Korea, United States and Canada. Sungmin Cha's co-authors include Taesup Moon, Juyeon Heo, Sungwoo Lee, Choong‐Wan Woo, Tor D. Wager, Hyun Suk Shin, Soonchul Kwon, Taeyoon Kim, Sungjun Cho and Youngsuk Jung and has published in prestigious journals such as ACS Applied Materials & Interfaces, Nature Protocols and IEEE Access.

In The Last Decade

Sungmin Cha

10 papers receiving 236 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sungmin Cha South Korea	8	74	66	61	34	30	12	238
Juan E. Arco Spain	12	72 1.0×	83 1.3×	103 1.7×	24 0.7×	63 2.1×	23	346
Zhijiang Wan China	9	39 0.5×	25 0.4×	160 2.6×	16 0.5×	30 1.0×	22	264
Mehmet Bilal Er Türkiye	9	94 1.3×	38 0.6×	51 0.8×	35 1.0×	23 0.8×	27	377
Nastaran Mohammadian Rad Netherlands	7	40 0.5×	45 0.7×	87 1.4×	61 1.8×	29 1.0×	12	242
Seyedehsamaneh Shojaeilangari Iran	9	22 0.3×	94 1.4×	30 0.5×	42 1.2×	15 0.5×	19	242
Ankita Singh India	9	73 1.0×	40 0.6×	47 0.8×	7 0.2×	8 0.3×	44	281
Muzaffar Bashir Pakistan	10	77 1.0×	102 1.5×	28 0.5×	28 0.8×	38 1.3×	16	375
Heba M. Emara Egypt	10	47 0.6×	47 0.7×	65 1.1×	14 0.4×	66 2.2×	21	208
Philipp Klumpp Germany	9	129 1.7×	36 0.5×	28 0.5×	26 0.8×	10 0.3×	24	280
Parvathavarthini Balasubramanian India	7	27 0.4×	50 0.8×	114 1.9×	21 0.6×	13 0.4×	12	294

Countries citing papers authored by Sungmin Cha

Since Specialization

Citations

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

Fields of papers citing papers by Sungmin Cha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sungmin Cha

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

12 of 12 papers shown

1.

Kwon, Yong‐Ju, Taeyang Kim, Jihwan Choi, et al.. (2025). Adsorption of Asymmetric and Linear Hazardous Gases on Graphene Oxides: Density Functional Study. C – Journal of Carbon Research. 11(1). 4–4. 1 indexed citations

2.

Cha, Sungmin, et al.. (2024). Learning to Unlearn: Instance-Wise Unlearning for Pre-trained Classifiers. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 11186–11194. 10 indexed citations

3.

Cha, Sungmin, et al.. (2024). NCIS: Neural Contextual Iterative Smoothing for Purifying Adversarial Perturbations. 3777–3787.

4.

Cha, Sungmin, et al.. (2023). Observations on K-Image Expansion of Image-Mixing Augmentation. IEEE Access. 11. 16631–16643. 1 indexed citations

5.

Yang, Sohee, et al.. (2023). Knowledge Unlearning for Mitigating Privacy Risks in Language Models. 14389–14408. 20 indexed citations

6.

Cha, Sungmin, et al.. (2023). Rebalancing Batch Normalization for Exemplar-Based Class-Incremental Learning. 20127–20136. 12 indexed citations

7.

Heo, Juyeon, Sungmin Cha, Sungwoo Lee, et al.. (2020). Toward a unified framework for interpreting machine-learning models in neuroimaging. Nature Protocols. 15(4). 1399–1435. 102 indexed citations

8.

Jung, Sangwon, et al.. (2020). Adaptive Group Sparse Regularization for Continual Learning..

9.

Cha, Sungmin, et al.. (2019). Uncertainty-based Continual Learning with Adaptive Regularization. Neural Information Processing Systems. 32. 4392–4402. 17 indexed citations

10.

Cha, Sungmin & Taesup Moon. (2019). Fully Convolutional Pixel Adaptive Image Denoiser. 4159–4168. 38 indexed citations

11.

Jung, Youngsuk, et al.. (2018). Enhanced Electrochemical Stability of a Zwitterionic-Polymer-Functionalized Electrode for Capacitive Deionization. ACS Applied Materials & Interfaces. 10(7). 6207–6217. 25 indexed citations

12.

Cha, Sungmin & Taesup Moon. (2018). Neural Adaptive Image Denoiser. 2981–2985. 12 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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