Xinhang Song

1.0k total citations
45 papers, 669 citations indexed

About

Xinhang Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Xinhang Song has authored 45 papers receiving a total of 669 indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Computer Vision and Pattern Recognition, 19 papers in Artificial Intelligence and 6 papers in Aerospace Engineering. Recurrent topics in Xinhang Song's work include Advanced Image and Video Retrieval Techniques (26 papers), Multimodal Machine Learning Applications (18 papers) and Domain Adaptation and Few-Shot Learning (13 papers). Xinhang Song is often cited by papers focused on Advanced Image and Video Retrieval Techniques (26 papers), Multimodal Machine Learning Applications (18 papers) and Domain Adaptation and Few-Shot Learning (13 papers). Xinhang Song collaborates with scholars based in China, Hong Kong and Spain. Xinhang Song's co-authors include Shuqiang Jiang, Luis Herranz, Gongwei Chen, Haitao Zeng, Xiaodan Zhang, Guoliang Fan, Chengpeng Chen, Boyue Wang, Junzhong Ji and Ramesh Jain and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Sensors.

In The Last Decade

Xinhang Song

42 papers receiving 648 citations

Peers

Xinhang Song
Comparison fields: 5 of 85
  • Computer Vision and Pattern Recognition 490
  • Artificial Intelligence 174
  • Biomedical Engineering 67
  • Aerospace Engineering 64
  • Media Technology 52
Replace Mohammadreza Babaee with:
Mohammadreza Babaee Germany
Yiming Cui United States
Luciano Oliveira Brazil
Kan Wu China
Leandro Cruz Portugal
Asaad Algarni Saudi Arabia
Mouazma Batool Pakistan
Hyunjong Park South Korea
Yan‐Pei Cao China
Mohammadreza Babaee Germany View profile →
Citations per field, relative to Xinhang Song
Xinhang Song · 1×
Citations per year, relative to Xinhang Song
Xinhang Song · 1×

Countries citing papers authored by Xinhang Song

Since Specialization
Citations

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

Fields of papers citing papers by Xinhang Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xinhang Song

This figure shows the co-authorship network connecting the top 25 collaborators of Xinhang Song. A scholar is included among the top collaborators of Xinhang Song based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xinhang Song. Xinhang Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 2
2 0
3 1
4 2
5 5
6 8
7 2
8 1
9 22
10 15
11
See More for Scene: Pairwise Consistency Learning for Scene Classification
3
12 12
13 13
14 51
15 62
16 8
17 14
18
Joint Learning of CNN and LSTM for Image Captioning.
3
19
MIAR ICT participation at Robot Vision 2013
4
20 7

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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