Zhanghui Kuang

2.0k citations
32 papers · 1.0k indexed · h-index 16

Zhanghui Kuang

31 papers receiving 1.0k citations

Peers

Zhanghui Kuang
Comparison fields: 5 of 86
  • Computer Vision and Pattern Recognition 894
  • Media Technology 141
  • Artificial Intelligence 352
  • Signal Processing 75
  • Human-Computer Interaction 38
Replace Aleksander Holynski with:
Aleksander Holynski United States
Mengyang Yu United Kingdom
Samuel Schulter United States
Aojun Zhou China
Ramakrishna Kakarala Singapore
Chung‐Ming Kuo Taiwan
Jon Hasselgren Sweden
Jamshid Shanbehzadeh Iran
Zhanghui Kuang relative to Aleksander Holynski United States Aleksander Holynski's profile →
Citations per field
00.5×10×20×30×
Aleksander Holynski · 1×
Citations per year

Countries citing papers authored by Zhanghui Kuang

Since Specialization
Citations

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

Fields of papers citing papers by Zhanghui Kuang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Zhanghui Kuang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Zhanghui Kuang Line = papers co-authored together Zhanghui Kuang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20223
2 202117
3
Towards Impartial Multi-task Learning
202116
4 20215
5 202112
6 202050
7 202016
8 201960
9 20196
10 201925
11 201835
12 20132
13 201216
14 2012127
15 20112
16 20100
17 200816
18 200215
19 200011
20 199912

About Zhanghui Kuang

Zhanghui Kuang is a scholar working on Computer Vision and Pattern Recognition, Radiation and Artificial Intelligence, having authored 32 papers that have together received 1.0k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (11 papers), Advanced Neural Network Applications (8 papers), Multimodal Machine Learning Applications (6 papers), Domain Adaptation and Few-Shot Learning (6 papers), Face recognition and analysis (5 papers), Visual Attention and Saliency Detection (4 papers), Video Surveillance and Tracking Methods (4 papers) and Generative Adversarial Networks and Image Synthesis (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (894 citations), Media Technology (141 citations) and Artificial Intelligence (352 citations). Zhanghui Kuang has collaborated with scholars based in China, Hong Kong and United Kingdom. Frequent co-authors include Wei Zhang, Kenneth K. Wong, Shuyang Sun, Lu Sheng, Wanli Ouyang, Hao Zhou, Jianyong Chen, Liyang Liu, Lingyu Liang and Lianwen Jin. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Expert Systems with Applications.

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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