Junfa Liu

2.8k citations
55 papers · 983 · h-index 19

Impact in

Papers in

Junfa Liu

54 papers receiving 958 citations

Peers

Junfa Liu
Comparison fields: 5 of 109
  • Endocrinology 101
  • Computer Vision and Pattern Recognition 337
  • Artificial Intelligence 360
  • Signal Processing 97
  • Human-Computer Interaction 38
Replace Yi An with:
Yi An China
Bailing Zhang China
Huimin Qian China
Qing Wang China
Yen-Ping Chen Taiwan
Yafeng Wu China
Karin Anna Hummel Austria
Cristian-Ioan Vasile United States
Yiyin Wang China
Koya Sato Japan
Junfa Liu relative to Yi An China Yi An's profile →
Citations per field
00.5×6.4×
Yi An · 1×
Citations per year

Countries citing papers authored by Junfa Liu

Since Specialization
Citations

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

Fields of papers citing papers by Junfa Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Junfa Liu, 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 Junfa Liu Line = papers co-authored together Junfa Liu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011111
2 201168
3 201568
4 200868
5 202165
6 200744
7 201343
8 200741
9 201535
10 201432
11 200631
12 200627
13 201126
14 201225
15 202122
16 200922
17 201322
18 201822
19 201419
20 201016

About Junfa Liu

Junfa Liu is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Artificial Intelligence, Computer Networks and Communications and Control and Systems Engineering, having authored 55 papers that have together received 983 indexed citations. Recurring topics across this work include Indoor and Outdoor Localization Technologies (16 papers), Machine Learning and ELM (11 papers), Human Pose and Action Recognition (7 papers), Energy Efficient Wireless Sensor Networks (7 papers), Yersinia bacterium, plague, ectoparasites research (6 papers), Context-Aware Activity Recognition Systems (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Human Motion and Animation (5 papers). The work is most often cited by research in Endocrinology (101 citations), Computer Vision and Pattern Recognition (337 citations), Artificial Intelligence (360 citations), Signal Processing (97 citations) and Human-Computer Interaction (38 citations). Junfa Liu has collaborated with scholars based in China, Singapore and Sweden. Frequent co-authors include Yiqiang Chen, Zhongtang Zhao, Xinlong Jiang, Matthew S. Francis, Petra J. Edqvist, Zhiqi Shen, Yang Gu, Mingjie Liu, Yang Gu and Mingjie Liu. Their work appears in journals such as Neurocomputing, Infection and Immunity, PLoS ONE, Poultry Science and Frontiers in Microbiology.

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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