Ming Wu

2.9k citations
86 papers · 2.1k · 2 hit papers · h-index 19

Impact in

Papers in

Ming Wu

79 papers receiving 2.0k citations

Ming Wu's Hit Papers

The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification 2020 · 291 citations
2910+2+5Years since publication200400600

Peers

Ming Wu
Comparison fields: 5 of 120
  • Computer Vision and Pattern Recognition 1.0k
  • Media Technology 395
  • Ocean Engineering 591
  • Environmental Engineering 494
  • Artificial Intelligence 560
Replace Xiaowei Shao with:
Xiaowei Shao China
Qi Zhao China
Renaud Marlet France
Junjue Wang China
Muhammad Shahzad Pakistan
Marius Muja Canada
Yang Zhang China
Keyan Chen China
Zhe Chen China
Ming Wu relative to Xiaowei Shao China Xiaowei Shao's profile →
Citations per field
00.5×1.5×2×2.4×
Xiaowei Shao · 1×
Citations per year

Countries citing papers authored by Ming Wu

Since Specialization
Citations

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

Fields of papers citing papers by Ming Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 86 papers — load more, or switch the sort, to bring in the rest.

#Work
1
D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction
Hit paper breakdown →
2018720
2
The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
Hit paper breakdown →
2020291
3 2014126
4 201591
5
NeuGraph: Parallel Deep Neural Network Computation on Large Graphs
201976
6 201973
7
Managing large graphs on multi-cores with graph awareness
201265
8 201937
9 202236
10 202031
11 201930
12
TUX 2 : distributed graph computation for machine learning
201729
13 202327
14 200625
15 202322
16 202122
17 202021
18 200919
19 201419
20 200916

About Ming Wu

Ming Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Ocean Engineering and Aerospace Engineering, having authored 86 papers that have together received 2.1k indexed citations. Recurring topics across this work include Robotic Path Planning Algorithms (8 papers), Robotics and Sensor-Based Localization (8 papers), Domain Adaptation and Few-Shot Learning (7 papers), Graph Theory and Algorithms (7 papers), Automated Road and Building Extraction (7 papers), Complex Network Analysis Techniques (7 papers), Advanced Vision and Imaging (7 papers) and Advanced Neural Network Applications (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.0k citations), Media Technology (395 citations), Ocean Engineering (591 citations), Environmental Engineering (494 citations) and Artificial Intelligence (560 citations). Ming Wu has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Chuang Zhang, Lidong Zhou, Jun Guo, Youshan Miao, Zhanyu Ma, Jiyang Xie, Dongliang Chang, Jilong Xue, Ayan Kumar Bhunia and Yifeng Ding. Their work appears in journals such as Remote Sensing, IEEE Access, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Engineering Applications of Artificial Intelligence and Computational Visual Media.

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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