Xin Geng
- Artificial Intelligence top 0.2%
- Computer Vision and Pattern Recognition top 0.2%
- Signal Processing top 0.5%
- Information Systems top 2%
- Experimental and Cognitive Psychology top 5%
- Co-authors
- Ning XuMin-Ling ZhangJianxin WuBin-Bin GaoJing WangChao XingYukun LiXia Yu
- Topics
- Text and Document Classification Technologies (65 papers)Face and Expression Recognition (27 papers)Image Retrieval and Classification Techniques (23 papers)
- Journals
- Energy & Environmental ScienceIEEE Transactions on Pattern Analysis and Machine IntelligenceChemistry of Materials
- Partner nations
- ChinaAustraliaUnited Kingdom
In The Last Decade
Xin Geng
135 papers receiving 4.2k citations
Hit Papers
Peers
Comparison fields: 5 of 154
- Artificial Intelligence 2.7k
- Computer Vision and Pattern Recognition 2.4k
- Signal Processing 833
- Information Systems 387
- Experimental and Cognitive Psychology 279
Countries citing papers authored by Xin Geng
This map shows the geographic impact of Xin Geng'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 Xin Geng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xin Geng more than expected).
Fields of papers citing papers by Xin Geng
This network shows the impact of papers produced by Xin Geng. 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 Xin Geng. The network helps show where Xin Geng may publish in the future.
Co-authorship network of co-authors of Xin Geng
This figure shows the co-authorship network connecting the top 25 collaborators of Xin Geng. A scholar is included among the top collaborators of Xin Geng 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 Xin Geng. Xin Geng is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 1 | |
| 5 | 3 | |
| 6 | 5 | |
| 7 | 1 | |
| 8 | 9 | |
| 9 | 5 | |
| 10 | 1 | |
| 11 | 9 | |
| 12 | 8 | |
| 13 | 21 | |
| 14 | 9 | |
| 15 | 16 | |
| 16 | Provably Consistent Partial-Label Learning | 3 |
| 17 | Liquid Milk: Cash Constraints and Day-to-Day Intertemporal Choice in Financial Diaries | 5 |
| 18 | 94 | |
| 19 | Sparsity conditional energy label distribution learning for age estimation | 11 |
| 20 | Pre-release prediction of crowd opinion on movies by label distribution learning | 63 |
About Xin Geng
Xin Geng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 145 papers that have together received 4.3k indexed citations. Recurring topics across this work include Text and Document Classification Technologies (65 papers), Face and Expression Recognition (27 papers) and Image Retrieval and Classification Techniques (23 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.4k citations), Artificial Intelligence (2.7k citations) and Signal Processing (833 citations). Xin Geng has collaborated with scholars based in China, Australia and United Kingdom. Frequent co-authors include Ning Xu, Min-Ling Zhang, Jianxin Wu, Bin-Bin Gao, Jing Wang, Chao Xing, Yukun Li, Xia Yu, Chen-Wei Xie and Xuying Liu. Their work appears in journals such as Energy & Environmental Science, IEEE Transactions on Pattern Analysis and Machine Intelligence and Chemistry of Materials.
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.