Long Sha

566 citations
29 papers · 315 · h-index 10

Impact in

Papers in

Long Sha

26 papers receiving 300 citations

Peers

Long Sha
Comparison fields: 5 of 103
  • Computer Vision and Pattern Recognition 124
  • Signal Processing 42
  • Orthopedics and Sports Medicine 30
  • Economics and Econometrics 84
  • Fuel Technology 2
Replace Yuliang Liu with:
Yuliang Liu China
Zhicheng Liu China
Yimei Yang China
Hongrui Liu China
Mingfei Sun China
Xuemei Luo China
Ting-Yi Chiang Taiwan
Simon Tanner United Kingdom
Zhaoran Liu China
Long Sha relative to Yuliang Liu China Yuliang Liu's profile →
Citations per field
00.5×
Yuliang Liu · 1×
Citations per year

Countries citing papers authored by Long Sha

Since Specialization
Citations

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

Fields of papers citing papers by Long Sha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201347
2 202241
3 202035
4 201928
5 201626
6 201822
7 201516
8 201416
9 202211
10 20219
11 20189
12 20179
13 20137
14 20226
15 20146
16 20235
17
Generating Multi-Agent Trajectories using Programmatic Weak Supervision
20184
18 20244
19 20234
20 20213

About Long Sha

Long Sha is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Plant Science, Signal Processing and Economics and Econometrics, having authored 29 papers that have together received 315 indexed citations. Recurring topics across this work include Video Analysis and Summarization (5 papers), Time Series Analysis and Forecasting (4 papers), Sports Analytics and Performance (3 papers), Human Pose and Action Recognition (3 papers), Plant Pathogens and Fungal Diseases (2 papers), Plant Virus Research Studies (2 papers), Video Surveillance and Tracking Methods (2 papers) and Metaheuristic Optimization Algorithms Research (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (124 citations), Signal Processing (42 citations), Orthopedics and Sports Medicine (30 citations), Economics and Econometrics (84 citations) and Fuel Technology (2 citations). Long Sha has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Patrick Lucey, Xinyu Wei, Sridha Sridharan, Stuart Morgan, Xiaorong Zou, Yisong Yue, Fan Cao, Xiaohan Ren, Yiannis A. Levendis and Peter Carr. Their work appears in journals such as Planta, Horticulturae, IET Renewable Power Generation, Journal of Fungi and Journal of Zhejiang University. Science A.

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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