Dan Song

2.1k citations
95 papers · 1.2k indexed · 1 hit paper · h-index 19

Dan Song

85 papers receiving 1.2k citations

Hit Papers

Illumination-aware faster R-CNN for robust multispectral ...3222018202620202023100200300

Peers

Dan Song
Comparison fields: 5 of 106
  • Computer Vision and Pattern Recognition 721
  • Media Technology 136
  • Computer Graphics and Computer-Aided Design 42
  • Geology 62
  • Control and Systems Engineering 249
Replace Jianhui Zhao with:
Jianhui Zhao China
Xinyu Zhang China
Sehoon Ha United States
Zhaoqi Wang China
Nick Pears United Kingdom
Xinyu Huang United States
Shohei Nobuhara Japan
Ran Song China
Jiang Yu Zheng United States
Ran Xu China
Dan Song relative to Jianhui Zhao China Jianhui Zhao's profile →
Citations per field
00.5×3.1×
Jianhui Zhao · 1×
Citations per year

Countries citing papers authored by Dan Song

Since Specialization
Citations

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

Fields of papers citing papers by Dan Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20250
3 20251
4 20240
5 20242
6 202411
7 20243
8 20241
9 20242
10 20235
11 20237
12 20235
13 20235
14 20231
15 202144
16 202014
17 20194
18 201810
19
Topological Synergies for Grasp Transfer
20131
20
Object Motion Analysis and Interpretation in Video
20040

About Dan Song

Dan Song is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Computational Mechanics, having authored 95 papers that have together received 1.2k indexed citations. Recurring topics across this work include 3D Shape Modeling and Analysis (29 papers), Advanced Image and Video Retrieval Techniques (22 papers), Human Pose and Action Recognition (20 papers), Robot Manipulation and Learning (13 papers), Domain Adaptation and Few-Shot Learning (9 papers), Robotics and Sensor-Based Localization (9 papers), Advanced Vision and Imaging (8 papers) and Image Retrieval and Classification Techniques (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (721 citations), Media Technology (136 citations) and Computer Graphics and Computer-Aided Design (42 citations). Dan Song has collaborated with scholars based in China, Sweden and Australia. Frequent co-authors include Ruofeng Tong, Min Tang, Chengyang Li, Danica Kragić, An-An Liu, Weizhi Nie, Wenhui Li, Kai Huebner, Carl Henrik Ek and Ville Kyrki. Their work appears in journals such as Scientific Reports, Applied Energy and IEEE Transactions on Geoscience and Remote Sensing.

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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