Kuan–Chuan Peng

1.5k total citations
21 papers, 446 citations indexed

About

Kuan–Chuan Peng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Kuan–Chuan Peng has authored 21 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 2 papers in Computer Networks and Communications. Recurrent topics in Kuan–Chuan Peng's work include Advanced Neural Network Applications (7 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Anomaly Detection Techniques and Applications (6 papers). Kuan–Chuan Peng is often cited by papers focused on Advanced Neural Network Applications (7 papers), Domain Adaptation and Few-Shot Learning (6 papers) and Anomaly Detection Techniques and Applications (6 papers). Kuan–Chuan Peng collaborates with scholars based in United States, Japan and Netherlands. Kuan–Chuan Peng's co-authors include Tsuhan Chen, Amir Sadovnik, Andrew Gallagher, Ziyan Wu, Lipeng Ke, Siwei Lyu, Yun Fu, Jan Ernst, Kunpeng Li and Amit K. Roy–Chowdhury and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, National University of Singapore and arXiv (Cornell University).

In The Last Decade

Kuan–Chuan Peng

19 papers receiving 435 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kuan–Chuan Peng United States 11 325 204 73 48 46 21 446
Min Peng China 11 253 0.8× 119 0.6× 29 0.4× 156 3.3× 25 0.5× 29 455
Luojun Lin China 11 377 1.2× 174 0.9× 43 0.6× 105 2.2× 44 1.0× 27 490
Dmitry Kit United States 9 217 0.7× 192 0.9× 104 1.4× 20 0.4× 15 0.3× 18 478
Sajid Ali Khan Pakistan 10 268 0.8× 66 0.3× 19 0.3× 94 2.0× 25 0.5× 21 354
Gloria Zen Italy 10 227 0.7× 129 0.6× 53 0.7× 140 2.9× 16 0.3× 14 400
Yuchi Liu China 7 141 0.4× 58 0.3× 46 0.6× 80 1.7× 62 1.3× 20 267
Subhabrata Bhattacharya United States 12 623 1.9× 135 0.7× 120 1.6× 30 0.6× 21 0.5× 19 695
Syaheerah Lebai Lutfi Malaysia 10 171 0.5× 106 0.5× 13 0.2× 75 1.6× 32 0.7× 42 342
Yante Li Finland 10 249 0.8× 127 0.6× 58 0.8× 263 5.5× 18 0.4× 20 475
Huai-Qian Khor Malaysia 6 254 0.8× 107 0.5× 43 0.6× 282 5.9× 24 0.5× 7 401

Countries citing papers authored by Kuan–Chuan Peng

Since Specialization
Citations

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

Fields of papers citing papers by Kuan–Chuan Peng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kuan–Chuan Peng. 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 Kuan–Chuan Peng. The network helps show where Kuan–Chuan Peng may publish in the future.

Co-authorship network of co-authors of Kuan–Chuan Peng

This figure shows the co-authorship network connecting the top 25 collaborators of Kuan–Chuan Peng. A scholar is included among the top collaborators of Kuan–Chuan Peng 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 Kuan–Chuan Peng. Kuan–Chuan Peng 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.
Li, Kaidong, et al.. (2025). PF3Det: A Prompted Foundation Feature Assisted Visual Lidar 3D Detector. 3778–3787. 1 indexed citations
2.
Lohit, Suhas, et al.. (2025). Multimodal 3D Object Detection on Unseen Domains. 2490–2500.
3.
Cherian, Anoop, et al.. (2024). Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads. 15779–15800. 1 indexed citations
4.
Peng, Kuan–Chuan, et al.. (2024). Long-Tailed Anomaly Detection with Learnable Class Names. 12435–12446. 6 indexed citations
5.
Peng, Kuan–Chuan, et al.. (2023). Cross-Domain Video Anomaly Detection without Target Domain Adaptation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2578–2590. 22 indexed citations
6.
7.
Peng, Kuan–Chuan, et al.. (2022). AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD). Directory of Open access Books (OAPEN Foundation). 1 indexed citations
8.
Ke, Lipeng, Kuan–Chuan Peng, & Siwei Lyu. (2022). Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition. Proceedings of the AAAI Conference on Artificial Intelligence. 36(1). 1131–1139. 37 indexed citations
9.
Peng, Kuan–Chuan. (2022). Iterative Self Knowledge Distillation — from Pothole Classification to Fine-Grained and Covid Recognition. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 3139–3143. 1 indexed citations
10.
Peng, Kuan–Chuan, et al.. (2021). Zero-shot Deep Domain Adaptation with Common Representation Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(7). 1–1. 10 indexed citations
11.
Li, Kunpeng, Ziyan Wu, Kuan–Chuan Peng, Jan Ernst, & Yun Fu. (2019). Guided Attention Inference Network. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(12). 2996–3010. 43 indexed citations
12.
Venkataramanan, Shashanka, et al.. (2019). Attention Guided Anomaly Detection and Localization in Images.. arXiv (Cornell University). 8 indexed citations
13.
Wu, Ziyan, et al.. (2019). Sharpen Focus: Learning With Attention Separability and Consistency. 512–521. 16 indexed citations
14.
Peng, Kuan–Chuan & Tsuhan Chen. (2016). Toward correlating and solving abstract tasks using convolutional neural networks. National University of Singapore. 1–9. 12 indexed citations
15.
Peng, Kuan–Chuan, Amir Sadovnik, Andrew Gallagher, & Tsuhan Chen. (2016). Where do emotions come from? Predicting the Emotion Stimuli Map. National University of Singapore. 614–618. 60 indexed citations
16.
Peng, Kuan–Chuan, Tsuhan Chen, Amir Sadovnik, & Andrew Gallagher. (2015). A mixed bag of emotions: Model, predict, and transfer emotion distributions. National University of Singapore. 860–868. 157 indexed citations
17.
Peng, Kuan–Chuan & Tsuhan Chen. (2015). Cross-layer features in convolutional neural networks for generic classification tasks. National University of Singapore. 3057–3061. 28 indexed citations
18.
Peng, Kuan–Chuan & Tsuhan Chen. (2015). A framework of extracting multi-scale features using multiple convolutional neural networks. National University of Singapore. 1–6. 20 indexed citations
19.
Peng, Kuan–Chuan, et al.. (2014). A framework of changing image emotion using emotion prediction. National University of Singapore. 4637–4641. 10 indexed citations
20.
Peng, Kuan–Chuan & Tsuhan Chen. (2013). Incorporating Cloud Distribution in Sky Representation. National University of Singapore. 91. 2152–2159. 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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