Nannan Li

807 total citations · 1 hit paper
24 papers, 543 citations indexed

About

Nannan Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Nannan Li has authored 24 papers receiving a total of 543 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Nannan Li's work include Human Pose and Action Recognition (11 papers), Anomaly Detection Techniques and Applications (7 papers) and Video Surveillance and Tracking Methods (5 papers). Nannan Li is often cited by papers focused on Human Pose and Action Recognition (11 papers), Anomaly Detection Techniques and Applications (7 papers) and Video Surveillance and Tracking Methods (5 papers). Nannan Li collaborates with scholars based in China, United States and Hong Kong. Nannan Li's co-authors include Ge Li, Shan Liu, Weijie Kong, Thomas H. Li, Jia-Xing Zhong, Jing Chen, Thomas Li, Huiwen Guo, Xinyu Wu and Jun Cheng and has published in prestigious journals such as IEEE Access, Molecules and Nanotechnology.

In The Last Decade

Nannan Li

22 papers receiving 529 citations

Hit Papers

Graph Convolutional Label Noise Cleaner: Train a Plug-And... 2019 2026 2021 2023 2019 100 200 300

Peers

Nannan Li
Comparison fields: 5 of 89
  • Artificial Intelligence 407
  • Computer Vision and Pattern Recognition 257
  • Computer Networks and Communications 236
  • Biomedical Engineering 121
  • Molecular Biology 26
Replace Geng Ji with:
Geng Ji China
Faizan Ullah Pakistan
Jehad M. Hamamreh Türkiye
A. Manikandan India
Jian Zheng China
Xiaoyang Wang China
Sameh A. Salem Egypt
Iñaki Estella Aguerri France
Jörg Hendrik Kappes Germany
Geng Ji China View profile →
Citations per field, relative to Nannan Li
Nannan Li · 1×
Citations per year, relative to Nannan Li
Nannan Li · 1×

Countries citing papers authored by Nannan Li

Since Specialization
Citations

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

Fields of papers citing papers by Nannan Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nannan Li

This figure shows the co-authorship network connecting the top 25 collaborators of Nannan Li. A scholar is included among the top collaborators of Nannan Li 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 Nannan Li. Nannan Li 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
# Work Indexed citations
1 1
2 0
3 1
4 1
5 0
6 13
7 1
8 3
9 7
10 46
11
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection breakdown →
376
12 1
13 20
14 3
15 5
16 7
17 12
18 1
19
Based on fuzzy neural network of multi-agent data fusion
1
20 29

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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