Tianfu Wu

3.3k total citations
79 papers, 1.5k citations indexed

About

Tianfu Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Tianfu Wu has authored 79 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Computer Vision and Pattern Recognition, 24 papers in Artificial Intelligence and 12 papers in Aerospace Engineering. Recurrent topics in Tianfu Wu's work include Advanced Image and Video Retrieval Techniques (27 papers), Advanced Neural Network Applications (17 papers) and Domain Adaptation and Few-Shot Learning (13 papers). Tianfu Wu is often cited by papers focused on Advanced Image and Video Retrieval Techniques (27 papers), Advanced Neural Network Applications (17 papers) and Domain Adaptation and Few-Shot Learning (13 papers). Tianfu Wu collaborates with scholars based in United States, China and United Kingdom. Tianfu Wu's co-authors include Song‐Chun Zhu, Gui-Song Xia, Wei Sun, Liangpei Zhang, Nan Xue, Liang Lin, Xianpeng Liu, Nan Xue, Khashayar Asadi and Song Bai and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, European Journal of Operational Research and IEEE Transactions on Image Processing.

In The Last Decade

Tianfu Wu

73 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tianfu Wu United States 21 982 300 218 171 120 79 1.5k
Martin Lauer Germany 22 938 1.0× 635 2.1× 339 1.6× 140 0.8× 126 1.1× 96 2.2k
Adam Herout Czechia 20 1.0k 1.1× 312 1.0× 131 0.6× 95 0.6× 95 0.8× 74 1.4k
Zsolt Kira United States 22 1.2k 1.2× 245 0.8× 765 3.5× 151 0.9× 58 0.5× 68 1.9k
Jiang Yu Zheng United States 19 972 1.0× 183 0.6× 118 0.5× 92 0.5× 74 0.6× 117 1.3k
Yuenan Hou China 16 856 0.9× 232 0.8× 319 1.5× 149 0.9× 42 0.3× 27 1.4k
Cristiano Premebida Portugal 21 1.0k 1.0× 424 1.4× 226 1.0× 113 0.7× 52 0.4× 62 1.6k
Seiichi Mita Japan 21 1.1k 1.1× 416 1.4× 180 0.8× 58 0.3× 92 0.8× 124 1.7k
Denis F. Wolf Brazil 20 811 0.8× 434 1.4× 192 0.9× 71 0.4× 74 0.6× 101 1.6k
Christian Schütz Switzerland 8 644 0.7× 286 1.0× 219 1.0× 88 0.5× 43 0.4× 23 1.1k
Di Feng Germany 10 738 0.8× 267 0.9× 325 1.5× 40 0.2× 47 0.4× 10 1.3k

Countries citing papers authored by Tianfu Wu

Since Specialization
Citations

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

Fields of papers citing papers by Tianfu Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tianfu Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Tianfu Wu. A scholar is included among the top collaborators of Tianfu Wu 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 Tianfu Wu. Tianfu Wu 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.
Liu, Xianpeng, et al.. (2025). DiffMesh: A Motion-Aware Diffusion Framework for Human Mesh Recovery from Videos. 4891–4901. 1 indexed citations
2.
Jin, Richeng, et al.. (2024). Sign-Based Gradient Descent With Heterogeneous Data: Convergence and Byzantine Resilience. IEEE Transactions on Neural Networks and Learning Systems. 36(2). 3834–3846. 6 indexed citations
3.
Xue, Nan, Bin Tan, Liang Dong, et al.. (2024). NEAT: Distilling 3D Wireframes from Neural Attraction Fields. 19968–19977. 1 indexed citations
4.
Wu, Tianfu, et al.. (2023). Learning Spatially-Adaptive Style-Modulation Networks for Single Image Synthesis. 1455–1459. 1 indexed citations
5.
Wu, Tianfu, et al.. (2023). Learning Spatially-Adaptive Squeeze-Excitation Networks for Few Shot Image Synthesis. 2855–2859. 2 indexed citations
6.
Baron, Dror, et al.. (2023). Generative Adversarial Network Based Adaptive Transmitter Modeling. 2183–2187.
7.
Krim, Hamid, et al.. (2022). Refining Self-Supervised Learning in Imaging: Beyond Linear Metric. 2022 IEEE International Conference on Image Processing (ICIP). 7. 76–80. 1 indexed citations
8.
9.
Wu, Tianfu, et al.. (2022). Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs. Sensors. 22(19). 7631–7631. 4 indexed citations
10.
11.
Krim, Hamid, et al.. (2021). Event driven sensor fusion. Signal Processing. 188. 108241–108241. 3 indexed citations
12.
Tan, Bin, Nan Xue, Song Bai, Tianfu Wu, & Gui-Song Xia. (2021). PlaneTR: Structure-Guided Transformers for 3D Plane Recovery. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 4166–4175. 37 indexed citations
13.
Zhang, Zekun & Tianfu Wu. (2020). Learning Ordered Top-k Adversarial Attacks via Adversarial Distillation. 3364–3373. 12 indexed citations
14.
Xue, Nan, Song Bai, Fudong Wang, et al.. (2019). Learning Attraction Field Representation for Robust Line Segment Detection. 1595–1603. 88 indexed citations
15.
Qi, Hang, Yuanlu Xu, Yuan Tao, Tianfu Wu, & Song‐Chun Zhu. (2018). Scene-Centric Joint Parsing of Cross-View Videos. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 11 indexed citations
16.
Asadi, Khashayar, et al.. (2018). Building an Integrated Mobile Robotic System for Real-Time Applications in Construction. Proceedings of the ... ISARC. 14 indexed citations
17.
Li, Xilai, et al.. (2017). AOGNets: Deep AND-OR Grammar Networks for Visual Recognition. arXiv (Cornell University). 5 indexed citations
18.
Zhao, Bo, Tao Wu, Tianfu Wu, & Yizhou Wang. (2016). Zero-Shot Learning via Revealing Data Distribution.. arXiv (Cornell University). 4 indexed citations
19.
Liu, Yong‐Jin, et al.. (2014). A global energy optimization framework for 2.1D sketch extraction from monocular images. Graphical Models. 76(5). 507–521. 15 indexed citations
20.
Gong, Haifeng, et al.. (2008). Deformable template combining alignable and non-alignable sketches. Proceedings - International Conference on Pattern Recognition. 1–4.

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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