Fangting Xia

683 citations
3 papers · 230 · h-index 3

Impact in

    • Advanced Neural Network Applications
    • Human Pose and Action Recognition
    • Multimodal Machine Learning Applications
    • Advanced Image and Video Retrieval Techniques
    • Video Surveillance and Tracking Methods
    • Domain Adaptation and Few-Shot Learning

Papers in

Fangting Xia

3 papers receiving 222 citations

Peers

Fangting Xia
Comparison fields: 5 of 42
  • Computer Vision and Pattern Recognition 158
  • Artificial Intelligence 64
  • Public Health, Environmental and Occupational Health 41
  • Human-Computer Interaction 8
  • Media Technology 10
Replace Arjun Karpur with:
Arjun Karpur United States
Zeman Shao United States
Estefanía Talavera Netherlands
Xiaoming Wei China
Preety Baglat Portugal
Di Miao China
Takumi Ege Japan
Alok Negi India
Sandhya Arora India
Haifa F. Alhasson Saudi Arabia
Fangting Xia relative to Arjun Karpur United States Arjun Karpur's profile →
Citations per field
00.5×2×4×5.6×
Arjun Karpur · 1×
Citations per year

Countries citing papers authored by Fangting Xia

Since Specialization
Citations

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

Fields of papers citing papers by Fangting Xia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 8 scholars most cited alongside Fangting Xia, 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 Fangting Xia Line = papers co-authored together Fangting Xia links everyone, so they are left out of the graph.

All Works

3 of 3 papers shown

About Fangting Xia

Fangting Xia is a scholar working on Computer Vision and Pattern Recognition, Public Health, Environmental and Occupational Health, Biomedical Engineering, Infectious Diseases and Organic Chemistry, having authored 3 papers that have together received 230 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (2 papers), Advanced Neural Network Applications (2 papers), Multimodal Machine Learning Applications (2 papers), Advanced Chemical Sensor Technologies (1 paper), Advanced Image and Video Retrieval Techniques (1 paper) and Nutritional Studies and Diet (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (158 citations), Artificial Intelligence (64 citations), Public Health, Environmental and Occupational Health (41 citations), Human-Computer Interaction (8 citations) and Media Technology (10 citations). Fangting Xia has collaborated with scholars based in United States. Frequent co-authors include Peng Wang, Alan Yuille, Liang-Chieh Chen, Jun Zhu, Arjun Karpur, Tobias Weyand, Jack Sim and Liviu Panait. Their work appears in journals such as Lecture notes in computer science and Proceedings of the AAAI Conference on Artificial Intelligence.

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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