Shentong Mo

827 total citations · 2 hit papers
21 papers, 320 citations indexed

About

Shentong Mo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Shentong Mo has authored 21 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 5 papers in Signal Processing. Recurrent topics in Shentong Mo's work include Music and Audio Processing (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Speech and Audio Processing (4 papers). Shentong Mo is often cited by papers focused on Music and Audio Processing (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Speech and Audio Processing (4 papers). Shentong Mo collaborates with scholars based in United States, China and United Arab Emirates. Shentong Mo's co-authors include Yapeng Tian, Yunhao Wang, Shumin Han, Gang Zeng, Ying Xin, Mingyu Ding, Xiaokang Chen, Xiaodi Wang, Ping Luo and Jingdong Wang and has published in prestigious journals such as Nature, Bioinformatics and International Journal of Computer Vision.

In The Last Decade

Shentong Mo

15 papers receiving 310 citations

Hit Papers

Context Autoencoder for Self-supervised Representation Le... 2023 2026 2024 2025 2023 2025 50 100 150

Peers

Shentong Mo
Comparison fields: 5 of 64
  • Computer Vision and Pattern Recognition 189
  • Artificial Intelligence 121
  • Signal Processing 61
  • Media Technology 22
  • Molecular Biology 21
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Musiur Raza Abidi India View profile →
Citations per field, relative to Shentong Mo
Shentong Mo · 1×
Citations per year, relative to Shentong Mo
Shentong Mo · 1×

Countries citing papers authored by Shentong Mo

Since Specialization
Citations

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

Fields of papers citing papers by Shentong Mo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shentong Mo

This figure shows the co-authorship network connecting the top 25 collaborators of Shentong Mo. A scholar is included among the top collaborators of Shentong Mo 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 Shentong Mo. Shentong Mo 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
A foundation model of transcription across human cell types breakdown →
26
2 1
3 0
4 2
5 4
6 8
7 0
8 2
9 1
10 0
11
Context Autoencoder for Self-supervised Representation Learning breakdown →
178
12 0
13 2
14 12
15 34
16 7
17 5
18 0
19 30
20 3

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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