Munan Ning

1.3k citations
14 papers · 480 · 1 hit paper · h-index 8

Impact in

Papers in

Munan Ning

14 papers receiving 477 citations

Munan Ning's Hit Papers

Video-LLaVA: Learning United Visual Representation by Alignment Before Projection 2024 · 73 citations
730+1Years since publication204060

Peers

Munan Ning
Comparison fields: 5 of 79
  • Computer Vision and Pattern Recognition 358
  • Artificial Intelligence 148
  • Radiology, Nuclear Medicine and Imaging 73
  • Biomedical Engineering 134
  • Media Technology 18
Replace Tri Huynh with:
Tri Huynh United States
Jiawen Yang China
Xianhui Liu China
Liulei Li China
Qingge Ji China
Zhezhou Yu China
Shuchao Pang China
Hongyang Xue China
Imran Fareed Nizami Pakistan
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Countries citing papers authored by Munan Ning

Since Specialization
Citations

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

Fields of papers citing papers by Munan Ning

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

14 of 14 papers shown
#Work
1 2020230
2
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
Hit paper breakdown →
202473
3 202259
4 202131
5 202323
6 202019
7 202010
8 20228
9 20256
10 20246
11 20245
12 20175
13 20234
14 20251

About Munan Ning

Munan Ning is a scholar working on Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Ophthalmology and Biomedical Engineering, having authored 14 papers that have together received 480 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (3 papers), Multimodal Machine Learning Applications (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Advanced Neural Network Applications (2 papers), COVID-19 diagnosis using AI (2 papers), Face recognition and analysis (2 papers), Retinal Imaging and Analysis (2 papers) and Gait Recognition and Analysis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (358 citations), Artificial Intelligence (148 citations), Radiology, Nuclear Medicine and Imaging (73 citations), Biomedical Engineering (134 citations) and Media Technology (18 citations). Munan Ning has collaborated with scholars based in China and United States. Frequent co-authors include Yaohua Wang, Yang Guo, Yefeng Zheng, Kai Ma, Bin Lin, Jiaxi Cui, Yang Ye, Bin Zhu, Yuan Li and Yuexiang Li. Their work appears in journals such as IEEE Transactions on Medical Imaging, IEEE Transactions on Pattern Analysis and Machine Intelligence, Image and Vision Computing, Pattern Recognition Letters 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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