Jilin Li

6.6k total citations · 4 hit papers
71 papers, 3.4k citations indexed

About

Jilin Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Organic Chemistry. According to data from OpenAlex, Jilin Li has authored 71 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Computer Vision and Pattern Recognition, 19 papers in Artificial Intelligence and 12 papers in Organic Chemistry. Recurrent topics in Jilin Li's work include Face recognition and analysis (20 papers), Anomaly Detection Techniques and Applications (10 papers) and Face and Expression Recognition (9 papers). Jilin Li is often cited by papers focused on Face recognition and analysis (20 papers), Anomaly Detection Techniques and Applications (10 papers) and Face and Expression Recognition (9 papers). Jilin Li collaborates with scholars based in China, Poland and United States. Jilin Li's co-authors include Feiyue Huang, Ying Tai, Chengjie Wang, Shouhong Ding, Taiping Yao, Yabiao Wang, Rongrong Ji, Donghao Luo, Honggen Wang and Shaoxin Li and has published in prestigious journals such as Chemical Communications, The Journal of Organic Chemistry and Chemistry - A European Journal.

In The Last Decade

Jilin Li

65 papers receiving 3.3k citations

Hit Papers

CurricularFace: Adaptive Curriculum Learning Loss for Dee... 2020 2026 2022 2024 2020 2021 2021 2022 100 200 300
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.

  1. CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition
    2020 340 citations Yuge Huang, Yuhan Wang et al. profile →
  2. Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework
    2021 198 citations Qingyu Song, Changan Wang et al. 2021 IEEE/CVF International Conference on Computer Vision (ICCV) profile →
  3. Local Relation Learning for Face Forgery Detection
    2021 195 citations Taiping Yao, Shouhong Ding et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
  4. Dual Contrastive Learning for General Face Forgery Detection
    2022 126 citations Taiping Yao, Shouhong Ding et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers

Jilin Li
Zeyi Huang United States
N. Sudha India
Yuxing Peng China
Xiaohong Liu China
Guangrun Wang China
Yen‐Cheng Liu Taiwan
Monika Sachdeva India
Ming Liu China
Jie Song China
Zeyi Huang United States
Jilin Li
Citations per year, relative to Jilin Li Jilin Li (= 1×) peers Zeyi Huang

Countries citing papers authored by Jilin Li

Since Specialization
Citations

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

Fields of papers citing papers by Jilin Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jilin Li

This figure shows the co-authorship network connecting the top 25 collaborators of Jilin Li. A scholar is included among the top collaborators of Jilin Li 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 Jilin Li. Jilin Li 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
1.
Zhang, Haotian, et al.. (2025). Anchor-aware representation learning for multi-view clustering. Neurocomputing. 656. 131441–131441.
2.
Chen, Yuanwei, et al.. (2024). Small Object Detection with Small Samples Using High-Resolution Remote Sensing Images. Journal of Physics Conference Series. 2890(1). 12012–12012. 1 indexed citations
3.
Gu, Zhihao, Yang Chen, Taiping Yao, et al.. (2022). Delving into the Local: Dynamic Inconsistency Learning for DeepFake Video Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 36(1). 744–752. 63 indexed citations
4.
Song, Qingyu, Changan Wang, Zhengkai Jiang, et al.. (2021). Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 3345–3354. 198 indexed citations breakdown →
5.
6.
Zhang, Ke-Yue, Taiping Yao, Shouhong Ding, et al.. (2021). An End-to-end Efficient Framework for Remote Physiological Signal Sensing. 2378–2384. 8 indexed citations
7.
Huang, Yuge, Pengcheng Shen, Shaoxin Li, et al.. (2021). Consistent Instance False Positive Improves Fairness in Face Recognition. 578–586. 29 indexed citations
8.
Ji, Xiaozhong, Yun Cao, Ying Tai, et al.. (2021). Frequency Consistent Adaptation for Real World Super Resolution. Proceedings of the AAAI Conference on Artificial Intelligence. 35(2). 1664–1672. 4 indexed citations
9.
Huang, Yuge, Pengcheng Shen, Ying Tai, et al.. (2020). Distribution Distillation Loss: Generic Approach for Improving Face Recognition from Hard Samples. arXiv (Cornell University). 1 indexed citations
10.
Liu, Liang, Jiangning Zhang, Ruifei He, et al.. (2020). Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation. 6488–6497. 110 indexed citations
11.
Huang, Yuge, Yuhan Wang, Ying Tai, et al.. (2020). CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition. 5900–5909. 340 indexed citations breakdown →
12.
Lin, En‐Ze, Jia‐Qiang Wu, Felix Schäfers, et al.. (2019). Author Correction: Regio- and stereoselective synthesis of tetra- and triarylethenes by N-methylimidodiacetyl boron-directed palladium-catalysed three-component coupling. Communications Chemistry. 2(1). 2 indexed citations
13.
Lin, En‐Ze, Jia‐Qiang Wu, Felix Schäfers, et al.. (2019). Regio- and stereoselective synthesis of tetra- and triarylethenes by N-methylimidodiacetyl boron-directed palladium-catalysed three-component coupling. Communications Chemistry. 2(1). 30 indexed citations
14.
Lv, Wen‐Xin, Zhan Li, En‐Ze Lin, et al.. (2019). Regio‐ and Diastereoselective Synthesis of Cyclohexadienylborons via an Intermolecular Diels–Alder Reaction of Alkenyl MIDA Boronates with 2‐Pyrones. Chemistry - A European Journal. 25(16). 4058–4061. 20 indexed citations
15.
Han, Xiang‐Lei, et al.. (2019). Cp*Co(III)-Catalyzed Dearomative [3 + 2] Spiroannulation of 2-Alkenylphenols with Ynamides via C–H Activation. The Journal of Organic Chemistry. 84(20). 12966–12974. 22 indexed citations
16.
17.
Li, Jilin, et al.. (2016). A Dynamic Method of Constructing MST Based on OpenFlow Protocol. International Journal of Computer and Communication Engineering. 5(6). 398–408.
18.
Hou, Xiaonan, Shouhong Ding, Lizhuang Ma, et al.. (2015). Similarity metric learning for face verification using sigmoid decision function. The Visual Computer. 32(4). 479–490. 11 indexed citations
19.
Fan, Zhigang, et al.. (2008). Local patterns constrained image histograms for image retrieval. 941–944. 9 indexed citations
20.
Li, Jilin. (2007). A FOA Estimation Approach for GALILEO/SAR Signal.

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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