Kai-Fu Yang

1.6k total citations
46 papers, 1.0k citations indexed

About

Kai-Fu Yang is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Media Technology. According to data from OpenAlex, Kai-Fu Yang has authored 46 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Computer Vision and Pattern Recognition, 12 papers in Cognitive Neuroscience and 11 papers in Media Technology. Recurrent topics in Kai-Fu Yang's work include Image Enhancement Techniques (14 papers), Visual Attention and Saliency Detection (13 papers) and Visual perception and processing mechanisms (11 papers). Kai-Fu Yang is often cited by papers focused on Image Enhancement Techniques (14 papers), Visual Attention and Saliency Detection (13 papers) and Visual perception and processing mechanisms (11 papers). Kai-Fu Yang collaborates with scholars based in China, Hong Kong and United States. Kai-Fu Yang's co-authors include Yongjie Li, Shaobing Gao, Chaoyi Li, Hongmei Yan, Chaoyi Li, Yubo Tan, Xian-Shi Zhang, Tao Deng, Hulin Kuang and Hui Li and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Kai-Fu Yang

45 papers receiving 996 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kai-Fu Yang China 19 731 227 213 192 167 46 1.0k
Yoichi Miyake Japan 19 568 0.8× 161 0.7× 204 1.0× 535 2.8× 87 0.5× 143 1.4k
Markku Hauta‐Kasari Finland 16 236 0.3× 186 0.8× 200 0.9× 237 1.2× 22 0.1× 86 872
Gareth Loy Australia 14 794 1.1× 235 1.0× 93 0.4× 20 0.1× 109 0.7× 25 1.2k
Meritxell Vilaseca Spain 20 104 0.1× 50 0.2× 584 2.7× 139 0.7× 124 0.7× 92 1.4k
Francisco Estrada Switzerland 4 3.0k 4.1× 522 2.3× 56 0.3× 29 0.2× 443 2.7× 4 3.2k
Ian E. McDowall United States 13 780 1.1× 700 3.1× 32 0.2× 188 1.0× 202 1.2× 47 1.4k
María Vanrell Spain 18 926 1.3× 201 0.9× 15 0.1× 165 0.9× 176 1.1× 54 1.2k
M.E. Jernigan Canada 12 309 0.4× 99 0.4× 53 0.2× 18 0.1× 87 0.5× 40 595
Alessandro Bria Italy 15 185 0.3× 116 0.5× 258 1.2× 28 0.1× 65 0.4× 44 1.1k
Hiroharu Kawanaka Japan 13 219 0.3× 30 0.1× 117 0.5× 27 0.1× 53 0.3× 115 601

Countries citing papers authored by Kai-Fu Yang

Since Specialization
Citations

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

Fields of papers citing papers by Kai-Fu Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kai-Fu Yang. 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 Kai-Fu Yang. The network helps show where Kai-Fu Yang may publish in the future.

Co-authorship network of co-authors of Kai-Fu Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Kai-Fu Yang. A scholar is included among the top collaborators of Kai-Fu Yang 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 Kai-Fu Yang. Kai-Fu Yang 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.
Yang, Kai-Fu, et al.. (2025). Weakly Supervised Micro- and Macro-Expression Spotting Based on Multi-Level Consistency. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(8). 6912–6928.
2.
Yang, Kai-Fu, et al.. (2024). Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization of Traffic Scenes. IEEE Transactions on Intelligent Transportation Systems. 25(11). 15841–15855. 2 indexed citations
3.
Li, Shun, Hao Li, Ningwei Sun, et al.. (2024). Basal actomyosin pulses expand epithelium coordinating cell flattening and tissue elongation. Nature Communications. 15(1). 3000–3000. 7 indexed citations
4.
Yang, Kai-Fu, et al.. (2024). Night-Time Vehicle Detection Based on Hierarchical Contextual Information. IEEE Transactions on Intelligent Transportation Systems. 25(10). 14628–14641. 4 indexed citations
5.
Shi, Yi, et al.. (2024). Weakly Supervised Fixated Object Detection in Traffic Videos Based on Driver’s Selective Attention Mechanism. IEEE Transactions on Circuits and Systems for Video Technology. 34(11). 11478–11492. 2 indexed citations
6.
Ruan, Xianhui, Wei Yan, Minghui Cao, et al.. (2024). Breast cancer cell-secreted miR-199b-5p hijacks neurometabolic coupling to promote brain metastasis. Nature Communications. 15(1). 4549–4549. 25 indexed citations
7.
Tan, Yubo, et al.. (2023). Deep matched filtering for retinal vessel segmentation. Knowledge-Based Systems. 283. 111185–111185. 26 indexed citations
8.
Tan, Yubo, et al.. (2023). A lightweight network guided with differential matched filtering for retinal vessel segmentation. Computers in Biology and Medicine. 160. 106924–106924. 11 indexed citations
9.
Sheng, Liang, W. P. Yan, Kai-Fu Yang, et al.. (2023). Dual-channel compressed ultrafast photography for Z-pinch dynamic imaging. Review of Scientific Instruments. 94(3). 35106–35106. 4 indexed citations
10.
Yang, Kai-Fu, et al.. (2022). Orientation and Context Entangled Network for Retinal Vessel Segmentation. SSRN Electronic Journal. 4 indexed citations
11.
Lin, Chuan, et al.. (2022). Learning generalized visual odometry using position-aware optical flow and geometric bundle adjustment. Pattern Recognition. 136. 109262–109262. 6 indexed citations
12.
Li, Shun, Kai-Fu Yang, Xiangyan Chen, et al.. (2021). Simultaneous 2D and 3D cell culture array for multicellular geometry, drug discovery and tumor microenvironment reconstruction. Biofabrication. 13(4). 45013–45013. 38 indexed citations
13.
Zhang, Xian-Shi, et al.. (2021). A Fish Retina-Inspired Single Image Dehazing Method. IEEE Transactions on Circuits and Systems for Video Technology. 32(4). 1875–1888. 12 indexed citations
14.
Li, Yongjie, et al.. (2020). Retinal fundus image enhancement with image decomposition and visual adaptation. Computers in Biology and Medicine. 128. 104116–104116. 41 indexed citations
15.
Zhang, Xian-Shi, Kai-Fu Yang, Jun Zhou, & Yongjie Li. (2020). Retina inspired tone mapping method for high dynamic range images. Optics Express. 28(5). 5953–5953. 7 indexed citations
16.
Liu, Yuhong, Kai-Fu Yang, & Hongmei Yan. (2019). No-Reference Image Quality Assessment Method Based on Visual Parameters. SHILAP Revista de lepidopterología. 17(2). 171–184. 8 indexed citations
17.
Deng, Tao, Kai-Fu Yang, Yongjie Li, & Hongmei Yan. (2016). Where Does the Driver Look? Top-Down-Based Saliency Detection in a Traffic Driving Environment. IEEE Transactions on Intelligent Transportation Systems. 17(7). 2051–2062. 68 indexed citations
18.
Yang, Kai-Fu, et al.. (2015). A Physiologically-Based Adaptive Three-Gaussian Function Model for Image Enhancement. 5(2). 72–79. 1 indexed citations
19.
Yang, Kai-Fu, et al.. (2015). Boundary Detection Using Double-Opponency and Spatial Sparseness Constraint. IEEE Transactions on Image Processing. 24(8). 2565–2578. 71 indexed citations
20.
Yang, Kai-Fu, Chaoyi Li, & Yongjie Li. (2015). Potential roles of the interaction between model V1 neurons with orientation-selective and non-selective surround inhibition in contour detection. Frontiers in Neural Circuits. 9. 30–30. 4 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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