Inkyu Shin

800 total citations · 1 hit paper
13 papers, 393 citations indexed

About

Inkyu Shin is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Inkyu Shin has authored 13 papers receiving a total of 393 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 2 papers in Control and Systems Engineering. Recurrent topics in Inkyu Shin's work include Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (7 papers) and Human Pose and Action Recognition (4 papers). Inkyu Shin is often cited by papers focused on Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (7 papers) and Human Pose and Action Recognition (4 papers). Inkyu Shin collaborates with scholars based in South Korea, Canada and United States. Inkyu Shin's co-authors include In So Kweon, Fei Pan, Seokju Lee, François Rameau, Kuk‐Jin Yoon, Sanghyun Woo, Buyu Liu, Yi‐Hsuan Tsai, Bingbing Zhuang and Samuel Schulter and has published in prestigious journals such as Journal of Geochemical Exploration, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

In The Last Decade

Inkyu Shin

13 papers receiving 388 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.

Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision

2020 248 citations Fei Pan, Inkyu Shin et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Inkyu Shin South Korea	7	300	247	65	37	31	13	393
Zongyan Han China	8	174 0.6×	257 1.0×	77 1.2×	18 0.5×	14 0.5×	13	378
Zak Murez United States	7	484 1.6×	219 0.9×	59 0.9×	61 1.6×	12 0.4×	8	600
Fei Pan China	7	286 1.0×	247 1.0×	60 0.9×	49 1.3×	6 0.2×	27	397
Christian Simon Australia	9	296 1.0×	390 1.6×	80 1.2×	54 1.5×	6 0.2×	18	489
Marco Toldo Italy	9	239 0.8×	252 1.0×	64 1.0×	17 0.5×	9 0.3×	12	375
Seonguk Seo South Korea	7	237 0.8×	187 0.8×	46 0.7×	15 0.4×	10 0.3×	13	407
Yuliang Zou United States	5	234 0.8×	161 0.7×	33 0.5×	15 0.4×	14 0.5×	8	285
Rui Gong Switzerland	6	227 0.8×	221 0.9×	44 0.7×	27 0.7×	5 0.2×	7	319
Pascal Mettes Netherlands	12	339 1.1×	167 0.7×	24 0.4×	21 0.6×	8 0.3×	38	434
Jinhong Deng China	7	304 1.0×	273 1.1×	63 1.0×	33 0.9×	4 0.1×	12	399

Countries citing papers authored by Inkyu Shin

Since Specialization

Citations

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

Fields of papers citing papers by Inkyu Shin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Inkyu Shin

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

13 of 13 papers shown

1.

Woo, Sanghyun, et al.. (2024). MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark. 22335–22346. 2 indexed citations

2.

Shin, Inkyu, Dahun Kim, Qihang Yu, et al.. (2024). Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation. 228–238. 5 indexed citations

3.

Shin, Inkyu, et al.. (2023). Bidirectional Domain Mixup for Domain Adaptive Semantic Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence. 37(1). 1114–1123. 7 indexed citations

4.

Shin, Inkyu, et al.. (2023). Learning Classifiers of Prototypes and Reciprocal Points for Universal Domain Adaptation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 531–540. 6 indexed citations

5.

Tremblay, Jonathan, Valts Blukis, Bowen Wen, et al.. (2023). TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation. 21285–21295. 18 indexed citations

6.

Shin, Inkyu, Kunyang Sun, Mateusz Koziński, et al.. (2023). MATE: Masked Autoencoders are Online 3D Test-Time Learners. 16663–16672. 6 indexed citations

7.

Shin, Inkyu, Yi‐Hsuan Tsai, Bingbing Zhuang, et al.. (2022). MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16907–16916. 34 indexed citations

8.

Shin, Inkyu, et al.. (2022). UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 14871–14880. 27 indexed citations

9.

Woo, Sanghyun, et al.. (2021). Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation. arXiv (Cornell University). 33. 10869–10880. 5 indexed citations

10.

Shin, Inkyu, et al.. (2021). Learning Representations by Contrasting Clusters While Bootstrapping Instances. 1 indexed citations

11.

Shin, Inkyu, et al.. (2021). LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 8568–8578. 26 indexed citations

12.

Pan, Fei, Inkyu Shin, François Rameau, Seokju Lee, & In So Kweon. (2020). Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision. 3763–3772. 248 indexed citations breakdown →

13.

Shin, Inkyu, Sunghyun Kim, & Dongbok Shin. (2016). Mineralogy and sulfur isotope compositions of the uraniferous black slates in the Ogcheon Metamorphic Belt, South Korea. Journal of Geochemical Exploration. 169. 1–12. 8 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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