Jifei Song

1.0k total citations
12 papers, 574 citations indexed

About

Jifei Song is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Control and Systems Engineering. According to data from OpenAlex, Jifei Song has authored 12 papers receiving a total of 574 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 3 papers in Computational Mechanics and 2 papers in Control and Systems Engineering. Recurrent topics in Jifei Song's work include Advanced Image and Video Retrieval Techniques (5 papers), Multimodal Machine Learning Applications (5 papers) and Advanced Vision and Imaging (4 papers). Jifei Song is often cited by papers focused on Advanced Image and Video Retrieval Techniques (5 papers), Multimodal Machine Learning Applications (5 papers) and Advanced Vision and Imaging (4 papers). Jifei Song collaborates with scholars based in United Kingdom, Sweden and China. Jifei Song's co-authors include Yi-Zhe Song, Timothy M. Hospedales, Tao Xiang, Qian Yu, Yongxin Yang, Kaiyue Pang, Richard A. Shaw, Eduardo Pérez-Pellitero, Yiren Zhou and Xiang Ruan and has published in prestigious journals such as IEEE Transactions on Image Processing, International Journal of Computer Vision and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Jifei Song

11 papers receiving 566 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jifei Song United Kingdom 9 528 104 89 59 39 12 574
Chun-Hao P. Huang Germany 9 359 0.7× 79 0.8× 46 0.5× 106 1.8× 38 1.0× 26 463
Aouaidjia Kamel China 6 247 0.5× 86 0.8× 77 0.9× 18 0.3× 9 0.2× 10 333
Xuecheng Nie China 14 569 1.1× 207 2.0× 95 1.1× 24 0.4× 8 0.2× 28 653
Zigang Geng China 5 291 0.6× 109 1.0× 60 0.7× 16 0.3× 5 0.1× 5 353
Daesik Jang South Korea 8 370 0.7× 69 0.7× 16 0.2× 11 0.2× 36 0.9× 19 435
Fuxiang Wu China 8 262 0.5× 79 0.8× 64 0.7× 10 0.2× 17 0.4× 24 410
Chongjian Ge Hong Kong 8 264 0.5× 74 0.7× 10 0.1× 57 1.0× 46 1.2× 9 350
Yipeng Qin United Kingdom 9 145 0.3× 54 0.5× 20 0.2× 68 1.2× 51 1.3× 34 273
Yuying Ge Hong Kong 7 446 0.8× 103 1.0× 40 0.4× 101 1.7× 56 1.4× 11 538
Yu‐Hui Wen China 7 227 0.4× 80 0.8× 81 0.9× 19 0.3× 15 0.4× 22 303

Countries citing papers authored by Jifei Song

Since Specialization
Citations

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

Fields of papers citing papers by Jifei Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jifei Song

This figure shows the co-authorship network connecting the top 25 collaborators of Jifei Song. A scholar is included among the top collaborators of Jifei Song 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 Jifei Song. Jifei Song is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Song, Jifei, et al.. (2024). Human Gaussian Splatting: Real-Time Rendering of Animatable Avatars. 788–798. 24 indexed citations
3.
Jung, Hyunjun, Guangyao Zhai, Yitong Li, et al.. (2023). On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks. 780–791. 6 indexed citations
4.
Verdié, Yannick, et al.. (2022). CroMo: Cross-Modal Learning for Monocular Depth Estimation. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3927–3937. 12 indexed citations
5.
Gryaditskaya, Yulia, Jifei Song, Yongxin Yang, et al.. (2021). Toward Fine-Grained Sketch-Based 3D Shape Retrieval. IEEE Transactions on Image Processing. 30. 8595–8606. 26 indexed citations
6.
Yu, Qian, Jifei Song, Yi-Zhe Song, Tao Xiang, & Timothy M. Hospedales. (2020). Fine-Grained Instance-Level Sketch-Based Image Retrieval. International Journal of Computer Vision. 129(2). 484–500. 31 indexed citations
7.
Song, Jifei, Yongxin Yang, Yi-Zhe Song, Tao Xiang, & Timothy M. Hospedales. (2019). Generalizable Person Re-Identification by Domain-Invariant Mapping Network. Edinburgh Research Explorer (University of Edinburgh). 719–728. 172 indexed citations
8.
Song, Jifei, Kaiyue Pang, Yi-Zhe Song, Tao Xiang, & Timothy M. Hospedales. (2018). Learning to Sketch with Shortcut Cycle Consistency. Edinburgh Research Explorer. 801–810. 69 indexed citations
9.
Song, Jifei, Qian Yu, Yi-Zhe Song, Tao Xiang, & Timothy M. Hospedales. (2017). Deep Spatial-Semantic Attention for Fine-Grained Sketch-Based Image Retrieval. Edinburgh Research Explorer (University of Edinburgh). 5552–5561. 169 indexed citations
10.
Song, Jifei, et al.. (2017). Fine-Grained Image Retrieval: the Text/Sketch Input Dilemma. Edinburgh Research Explorer (University of Edinburgh). 36 indexed citations
11.
Song, Jifei, Yi-Zhe Song, Tao Xiang, Timothy M. Hospedales, & Xiang Ruan. (2016). Deep Multi-task Attribute-driven Ranking for Fine-grained Sketch-based Image Retrieval. Edinburgh Research Explorer. 132.1–132.11. 21 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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