Xin Geng

6.5k total citations · 3 hit papers
145 papers, 4.3k citations indexed

About

Xin Geng is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Xin Geng has authored 145 papers receiving a total of 4.3k indexed citations (citations by other indexed papers that have themselves been cited), including 92 papers in Artificial Intelligence, 87 papers in Computer Vision and Pattern Recognition and 22 papers in Signal Processing. Recurrent topics in Xin Geng's work include Text and Document Classification Technologies (65 papers), Face and Expression Recognition (27 papers) and Image Retrieval and Classification Techniques (23 papers). Xin Geng is often cited by papers focused on Text and Document Classification Technologies (65 papers), Face and Expression Recognition (27 papers) and Image Retrieval and Classification Techniques (23 papers). Xin Geng collaborates with scholars based in China, Australia and United Kingdom. Xin Geng's 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 and has published in prestigious journals such as Energy & Environmental Science, IEEE Transactions on Pattern Analysis and Machine Intelligence and Chemistry of Materials.

In The Last Decade

Xin Geng

135 papers receiving 4.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Label Distribution Learning

2016 466 citations Xin Geng IEEE Transactions on Knowledge and Data Engineering profile →
Deep Label Distribution Learning With Label Ambiguity

2017 349 citations Xin Geng et al. profile →
Binary relevance for multi-label learning: an overview

2017 276 citations Xin Geng et al. profile →

Peers

Xin Geng

AI

CVPR

SP

IS

ECP

Zichao Yang United States

Andrew B. Goldberg United States

Martin Karafiát Czechia

Rich Zemel Canada

Michele Nappi Italy

Adel Elmaghraby United States

Ryan Kiros Canada

Chong‐Wah Ngo Hong Kong

Chaitanya Ahuja United States

Kelvin Xu United States

Zichao Yang United States

Xin Geng

3.5k ×1.3

AI

1.7k ×0.7

CVPR

285 ×0.3

SP

677 ×1.7

IS

120 ×0.4

ECP

Citations per year, relative to Xin Geng Xin Geng (= 1×) peers Zichao Yang

Countries citing papers authored by Xin Geng

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Geng, Xin, et al.. (2025). SSL-OHE: A self-supervised ensemble approach for early diagnosis of biliary atresia from sonographic images. Biomedical Signal Processing and Control. 112. 108539–108539.

2.

Geng, Xin, et al.. (2025). Queue layouts on folded hypercubes. Discrete Applied Mathematics. 373. 154–158.

3.

Gu, Ziyuan, et al.. (2025). Topological Information Utilization in Label Enhancement and Label Distribution Learning Based on Optimal Transport Theory. IEEE Transactions on Knowledge and Data Engineering. 37(9). 5666–5678.

4.

Huang, Hao, Hongxu Zhou, Hongwei Zhao, et al.. (2025). Architecting Host–Guest Synergistic Solid-State Electrolytes Enables Unobstructed Li-Ion Interphase Migration for Lithium Metal Batteries. Chemistry of Materials. 37(4). 1393–1402. 1 indexed citations

5.

Wang, Jing, Feng Fu, Jianhui Lv, & Xin Geng. (2024). Residual k-Nearest Neighbors Label Distribution Learning. Pattern Recognition. 158. 111006–111006. 3 indexed citations

6.

Chen, Sheng, et al.. (2024). Label enhancement via manifold approximation and projection with graph convolutional network. Pattern Recognition. 152. 110447–110447. 5 indexed citations

7.

Wang, Hongsong, et al.. (2024). Addressing Skewed Heterogeneity via Federated Prototype Rectification With Personalization. IEEE Transactions on Neural Networks and Learning Systems. 36(5). 8442–8454. 1 indexed citations

8.

Yuan, Jin, Y.S. Zhang, Zhongchao Shi, et al.. (2024). Domain-Aware Graph Network for Bridging Multi-Source Domain Adaptation. IEEE Transactions on Multimedia. 26. 7210–7224. 9 indexed citations

9.

Qi, Lei, et al.. (2024). A Novel Cross-Perturbation for Single Domain Generalization. IEEE Transactions on Circuits and Systems for Video Technology. 34(11). 10903–10916. 5 indexed citations

10.

Li, Xiangyi, Jiajia Guo, Chao-Kai Wen, Xin Geng, & Shi Jin. (2024). Facilitating AI-Based CSI Feedback Deployment in Massive MIMO Systems With Learngene. IEEE Transactions on Wireless Communications. 23(9). 11325–11340. 1 indexed citations

11.

Qi, Lei, et al.. (2024). MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization. ACM Transactions on Multimedia Computing Communications and Applications. 20(6). 1–21. 9 indexed citations

12.

Geng, Xin, et al.. (2023). Motional foreground attention-based video crowd counting. Pattern Recognition. 144. 109891–109891. 8 indexed citations

13.

Qi, Lei, Lei Wang, Jing Huo, et al.. (2021). Adversarial Camera Alignment Network for Unsupervised Cross-Camera Person Re-Identification. IEEE Transactions on Circuits and Systems for Video Technology. 32(5). 2921–2936. 21 indexed citations

14.

Chen, Sheng, et al.. (2021). A Novel Probabilistic Label Enhancement Algorithm for Multi-Label Distribution Learning. IEEE Transactions on Knowledge and Data Engineering. 34(11). 5098–5113. 9 indexed citations

15.

Chen, Sheng, et al.. (2020). Multilabel Distribution Learning Based on Multioutput Regression and Manifold Learning. IEEE Transactions on Cybernetics. 52(6). 5064–5078. 16 indexed citations

16.

Feng, Lei, Jiaqi Lv, Bo Han, et al.. (2020). Provably Consistent Partial-Label Learning. Neural Information Processing Systems. 33. 10948–10960. 3 indexed citations

17.

Geng, Xin, Wendy Janssens, & Berber Kramer. (2017). Liquid Milk: Cash Constraints and Day-to-Day Intertemporal Choice in Financial Diaries. Data Archiving and Networked Services (DANS). 5 indexed citations

18.

Zhou, Deyu, et al.. (2016). Emotion Distribution Learning from Texts. 638–647. 94 indexed citations

19.

Yang, Xu, Xin Geng, & Deyu Zhou. (2016). Sparsity conditional energy label distribution learning for age estimation. International Joint Conference on Artificial Intelligence. 2259–2265. 11 indexed citations

20.

Geng, Xin & Peng Hou. (2015). Pre-release prediction of crowd opinion on movies by label distribution learning. International Conference on Artificial Intelligence. 3511–3517. 63 indexed citations

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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