Kun-Yu Lin

662 total citations · 1 hit paper
24 papers, 330 citations indexed

About

Kun-Yu Lin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Kun-Yu Lin has authored 24 papers receiving a total of 330 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 12 papers in Computer Vision and Pattern Recognition and 4 papers in Control and Systems Engineering. Recurrent topics in Kun-Yu Lin's work include Human Pose and Action Recognition (7 papers), Anomaly Detection Techniques and Applications (7 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Kun-Yu Lin is often cited by papers focused on Human Pose and Action Recognition (7 papers), Anomaly Detection Techniques and Applications (7 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Kun-Yu Lin collaborates with scholars based in China, United States and Australia. Kun-Yu Lin's co-authors include Wei‐Shi Zheng, Yaowei Wang, J. Andy, Chih‐Lin Hu, Carl K. Chang, Chang‐Dong Wang, Ling Huang, Longbing Cao, Xiao-Ming Wu and Zelin Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Information Sciences and IEEE Transactions on Cybernetics.

In The Last Decade

Kun-Yu Lin

21 papers receiving 325 citations

Hit Papers

DilateFormer: Multi-Scale Dilated Transformer for Visual ... 2023 2026 2024 2025 2023 50 100 150
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. DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition
    2023 152 citations Kun-Yu Lin, J. Andy et al. IEEE Transactions on Multimedia profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kun-Yu Lin China 9 144 125 36 32 29 24 330
Haoyun Sun China 7 109 0.8× 207 1.7× 17 0.5× 30 0.9× 13 0.4× 18 387
Petru Soviany Romania 2 140 1.0× 127 1.0× 12 0.3× 15 0.5× 9 0.3× 2 328
Yiyu Qiu Australia 4 259 1.8× 263 2.1× 33 0.9× 15 0.5× 9 0.3× 6 489
Sheng‐Fuu Lin Taiwan 8 283 2.0× 190 1.5× 40 1.1× 16 0.5× 9 0.3× 36 422
Zakria Zakria China 10 133 0.9× 110 0.9× 61 1.7× 39 1.2× 5 0.2× 21 314
Nanfeng Xiao China 9 166 1.2× 115 0.9× 28 0.8× 29 0.9× 4 0.1× 80 395
Ligeng Zhu United States 9 232 1.6× 203 1.6× 16 0.4× 51 1.6× 9 0.3× 13 420
Vitaly Ablavsky United States 13 351 2.4× 101 0.8× 23 0.6× 44 1.4× 10 0.3× 28 433
Massimiliano Mancini Italy 11 292 2.0× 438 3.5× 35 1.0× 30 0.9× 8 0.3× 29 607
Mohammad Kazem Moghimi Iran 10 290 2.0× 178 1.4× 46 1.3× 64 2.0× 7 0.2× 21 450

Countries citing papers authored by Kun-Yu Lin

Since Specialization
Citations

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

Fields of papers citing papers by Kun-Yu Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kun-Yu Lin. 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 Kun-Yu Lin. The network helps show where Kun-Yu Lin may publish in the future.

Co-authorship network of co-authors of Kun-Yu Lin

This figure shows the co-authorship network connecting the top 25 collaborators of Kun-Yu Lin. A scholar is included among the top collaborators of Kun-Yu Lin 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 Kun-Yu Lin. Kun-Yu Lin 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.
Lin, Kun-Yu, et al.. (2025). Mitigating the Human-Robot Domain Discrepancy in Visual Pre-training for Robotic Manipulation. 22551–22561. 1 indexed citations
2.
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Lin, Kun-Yu, et al.. (2024). Human-Centric Transformer for Domain Adaptive Action Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(2). 679–696. 2 indexed citations
5.
Lin, Kun-Yu, Fa-Ting Hong, Zhongang Qi, et al.. (2024). Weakly-Supervised Temporal Action Localization by Progressive Complementary Learning. IEEE Transactions on Circuits and Systems for Video Technology. 35(1). 938–952. 2 indexed citations
6.
Wu, Xiao-Ming, et al.. (2023). Generating Anomalies for Video Anomaly Detection with Prompt-based Feature Mapping. 24500–24510. 18 indexed citations
7.
Lin, Kun-Yu, Peiyi Liu, Po‐Kai Wang, Chih‐Lin Hu, & Ying Cai. (2023). Predicting Road Traffic Risks with CNN-and-LSTM Learning Over Spatio-Temporal and Multi-Feature Traffic Data. 305–311. 2 indexed citations
8.
Lin, Kun-Yu, et al.. (2023). Event-Guided Procedure Planning from Instructional Videos with Text Supervision. 13519–13529. 6 indexed citations
9.
Lin, Kun-Yu, et al.. (2023). AsyFOD: An Asymmetric Adaptation Paradigm for Few-Shot Domain Adaptive Object Detection. 3261–3271. 11 indexed citations
10.
Lin, Kun-Yu, et al.. (2023). DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition. IEEE Transactions on Multimedia. 25. 8906–8919. 152 indexed citations breakdown →
11.
Cao, Longbing, et al.. (2023). Out-of-Distribution Detection by Cross-Class Vicinity Distribution of In-Distribution Data. IEEE Transactions on Neural Networks and Learning Systems. 35(10). 13777–13788. 3 indexed citations
12.
Lin, Kun-Yu, et al.. (2023). TwinFormer: Fine-to-Coarse Temporal Modeling for Long-Term Action Recognition. IEEE Transactions on Multimedia. 26. 2715–2728. 2 indexed citations
13.
Lin, Kun-Yu, et al.. (2023). Generalized Intra-Camera Supervised Person Re-Identification. IEEE Transactions on Circuits and Systems for Video Technology. 34(6). 4516–4527. 1 indexed citations
14.
Cao, Longbing, et al.. (2023). Supervision Adaptation Balancing In-Distribution Generalization and Out-of-Distribution Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 15743–15758. 2 indexed citations
15.
Cao, Longbing, et al.. (2022). Revealing the Distributional Vulnerability of Discriminators by Implicit Generators. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(7). 1–13. 7 indexed citations
16.
Chen, Zelin, Kun-Yu Lin, & Wei‐Shi Zheng. (2022). Consistent Intra-Video Contrastive Learning With Asynchronous Long-Term Memory Bank. IEEE Transactions on Circuits and Systems for Video Technology. 33(3). 1168–1180. 9 indexed citations
17.
Wang, Chang‐Dong, et al.. (2020). Node Pair Information Preserving Network Embedding Based on Adversarial Networks. IEEE Transactions on Cybernetics. 52(7). 5908–5922. 11 indexed citations
18.
Lin, Kun-Yu, et al.. (2018). Analysis of Retrieving Publications with Multiple Citation Networks. 1–6. 2 indexed citations
19.
Huang, Ling, et al.. (2018). Direction recovery in undirected social networks based on community structure and popularity. Information Sciences. 473. 31–43. 8 indexed citations
20.
Chen, Chao, Kun-Yu Lin, Chang‐Dong Wang, Jianbo Liu, & Dong Huang. (2017). CCMS: A nonlinear clustering method based on crowd movement and selection. Neurocomputing. 269. 120–131. 3 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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