Beibei Liu

721 citations
33 papers · 446 indexed · h-index 12
Topics
Digital Media Forensic Detection (15 papers)Advanced Steganography and Watermarking Techniques (9 papers)Generative Adversarial Networks and Image Synthesis (6 papers)

In The Last Decade

Beibei Liu

29 papers receiving 432 citations

Peers

Beibei Liu
Comparison fields: 5 of 67
  • Computer Vision and Pattern Recognition 305
  • Human-Computer Interaction 144
  • Control and Systems Engineering 89
  • Cognitive Neuroscience 53
  • Artificial Intelligence 43
Replace E. Kiran Kumar with:
E. Kiran Kumar India
Xiangdong Li China
Yen-Lun Chen China
Malik Mallem France
Dharmpal D. Doye India
Grégory Rogez Spain
Stephen Karungaru Japan
Atul Kanaujia United States
Youngwoo Yoon South Korea
Beibei Liu relative to E. Kiran Kumar India E. Kiran Kumar's profile →
Citations per field
00.5×10×13×
E. Kiran Kumar · 1×
Citations per year

Countries citing papers authored by Beibei Liu

Since Specialization
Citations

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

Fields of papers citing papers by Beibei Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Beibei Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Beibei Liu. A scholar is included among the top collaborators of Beibei Liu 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 Beibei Liu. Beibei Liu 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
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Fast Image Artistic Style Learning Using Twin-Codebook Vector Quantization
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Mathematical Modeling in Composing Test Paper Based on Reasoning and Its Application
1

About Beibei Liu

Beibei Liu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing, having authored 33 papers that have together received 446 indexed citations. Recurring topics across this work include Digital Media Forensic Detection (15 papers), Advanced Steganography and Watermarking Techniques (9 papers) and Generative Adversarial Networks and Image Synthesis (6 papers). The work is most often cited by research in Human-Computer Interaction (144 citations), Computer Vision and Pattern Recognition (305 citations) and Control and Systems Engineering (89 citations). Beibei Liu has collaborated with scholars based in China, Australia and United Kingdom. Frequent co-authors include Yongjian Hu, Yuting Ye, Shangchen Han, Christopher D. Twigg, Heung-Kyu Lee, Robert Wang, Kenrick Kin, Chang-Hee Choi, Chengde Wan and Peizhao Zhang. Their work appears in journals such as IEEE Access, ACM Transactions on Graphics and Pattern Recognition.

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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