Yulei Niu

2.6k total citations · 3 hit papers
27 papers, 1.4k citations indexed

About

Yulei Niu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Mechanics of Materials. According to data from OpenAlex, Yulei Niu has authored 27 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Artificial Intelligence, 19 papers in Computer Vision and Pattern Recognition and 3 papers in Mechanics of Materials. Recurrent topics in Yulei Niu's work include Multimodal Machine Learning Applications (15 papers), Domain Adaptation and Few-Shot Learning (12 papers) and Topic Modeling (7 papers). Yulei Niu is often cited by papers focused on Multimodal Machine Learning Applications (15 papers), Domain Adaptation and Few-Shot Learning (12 papers) and Topic Modeling (7 papers). Yulei Niu collaborates with scholars based in China, Singapore and United States. Yulei Niu's co-authors include Hanwang Zhang, Kaihua Tang, Jianqiang Huang, Jiaxin Shi, Ji-Rong Wen, Shih‐Fu Chang, Zhiwu Lu, Xian‐Sheng Hua, Beier Zhu and Zhiwu Lu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and RSC Advances.

In The Last Decade

Yulei Niu

25 papers receiving 1.4k citations

Hit Papers

Unbiased Scene Graph Generation From Biased Training 2020 2026 2022 2024 2020 2021 2023 100 200 300 400

Peers

Yulei Niu
Comparison fields: 5 of 92
  • Computer Vision and Pattern Recognition 1.1k
  • Artificial Intelligence 935
  • Media Technology 50
  • Information Systems 43
  • Aerospace Engineering 39
Replace Xide Xia with:
Xide Xia United States
Kaihua Tang China
Mahsa Baktashmotlagh Australia
Zhao-Min Chen China
Justin Johnson United States
Qinxun Bai United States
Zicheng Liu China
Defang Chen China
Han Zhao United States
Xide Xia United States View profile →
Citations per field, relative to Yulei Niu
Yulei Niu · 1×
Citations per year, relative to Yulei Niu
Yulei Niu · 1×

Countries citing papers authored by Yulei Niu

Since Specialization
Citations

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

Fields of papers citing papers by Yulei Niu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yulei Niu

This figure shows the co-authorship network connecting the top 25 collaborators of Yulei Niu. A scholar is included among the top collaborators of Yulei Niu 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 Yulei Niu. Yulei Niu 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 0
2 0
3 1
4 10
5 2
6 8
7 2
8 23
9 6
10 19
11 5
12
Counterfactual VQA: A Cause-Effect Look at Language Bias breakdown →
266
13
Unbiased Scene Graph Generation From Biased Training breakdown →
429
14 88
15 5
16 12
17 159
18 4
19 4
20
Weakly supervised matrix factorization for noisily tagged image parsing
10

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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