Lingxiao Song

1.1k citations
17 papers · 659 indexed · h-index 10
Topics
Face recognition and analysis (11 papers)Generative Adversarial Networks and Image Synthesis (6 papers)Face and Expression Recognition (6 papers)
Partner nations
ChinaUnited Kingdom

In The Last Decade

Lingxiao Song

16 papers receiving 638 citations

Peers

Lingxiao Song
Comparison fields: 5 of 52
  • Computer Vision and Pattern Recognition 595
  • Signal Processing 217
  • Artificial Intelligence 59
  • Media Technology 28
  • Experimental and Cognitive Psychology 21
Replace Gilbert Maître with:
Gilbert Maître Switzerland
Gabriele Sabatino Italy
Song-Chun Zhu United States
Jae‐Joon Han South Korea
Quan Wang China
Rama Chellappa United States
Ryoichi Komiya Malaysia
Zhiheng Niu China
Xiaozheng Zhang Australia
Lingxiao Song relative to Gilbert Maître Switzerland Gilbert Maître's profile →
Citations per field
00.5×10×14×
Gilbert Maître · 1×
Citations per year

Countries citing papers authored by Lingxiao Song

Since Specialization
Citations

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

Fields of papers citing papers by Lingxiao Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lingxiao Song

This figure shows the co-authorship network connecting the top 25 collaborators of Lingxiao Song. A scholar is included among the top collaborators of Lingxiao Song 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 Lingxiao Song. Lingxiao Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

17 of 17 papers shown
#WorkIndexed citations
1 1
2 0
3 12
4 5
5 2
6 3
7 32
8 109
9 24
10 64
11 85
12 9
13 89
14 83
15 29
16
Masquer Hunter: Adversarial Occlusion-aware Face Detection
4
17 108

About Lingxiao Song

Lingxiao Song is a scholar working on Computer Vision and Pattern Recognition, Neurology and Signal Processing, having authored 17 papers that have together received 659 indexed citations. Recurring topics across this work include Face recognition and analysis (11 papers), Generative Adversarial Networks and Image Synthesis (6 papers) and Face and Expression Recognition (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (595 citations), Signal Processing (217 citations) and Media Technology (28 citations). Lingxiao Song has collaborated with scholars based in China and United Kingdom. Frequent co-authors include Ran He, Tieniu Tan, Xiang Wu, Zhenan Sun, Jie Cao, Man Zhang, Zhihe Lu, Yi Li, Linsen Song and Yibo Hu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and Frontiers in Neuroscience.

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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