Gusi Te

539 total citations
2 papers, 229 citations indexed

About

Gusi Te is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Artificial Intelligence. According to data from OpenAlex, Gusi Te has authored 2 papers receiving a total of 229 indexed citations (citations by other indexed papers that have themselves been cited), including 1 paper in Computer Vision and Pattern Recognition, 1 paper in Computational Mechanics and 1 paper in Artificial Intelligence. Recurrent topics in Gusi Te's work include 3D Surveying and Cultural Heritage (1 paper), Face recognition and analysis (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). Gusi Te is often cited by papers focused on 3D Surveying and Cultural Heritage (1 paper), Face recognition and analysis (1 paper) and Domain Adaptation and Few-Shot Learning (1 paper). Gusi Te collaborates with scholars based in China. Gusi Te's co-authors include Wei Hu, Amin Zheng, Zongming Guo, Yinglu Liu, Hailin Shi and Tao Mei and has published in prestigious journals such as IEEE Transactions on Image Processing.

In The Last Decade

Gusi Te

2 papers receiving 221 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gusi Te China	2	143	109	96	89	43	2	229
Amin Zheng Hong Kong	7	148 1.0×	109 1.0×	128 1.3×	89 1.0×	43 1.0×	14	266
Zizheng Yan China	6	106 0.7×	78 0.7×	133 1.4×	59 0.7×	32 0.7×	9	268
Ziyin Zeng China	10	134 0.9×	130 1.2×	73 0.8×	130 1.5×	30 0.7×	21	248
Yachao Zhang China	9	145 1.0×	131 1.2×	132 1.4×	125 1.4×	14 0.3×	25	291
Tung M. Luu South Korea	6	85 0.6×	70 0.6×	100 1.0×	55 0.6×	24 0.6×	11	206
Cho-Ying Wu United States	6	136 1.0×	95 0.9×	150 1.6×	68 0.8×	45 1.0×	9	272
Francis Engelmann United States	7	67 0.5×	66 0.6×	119 1.2×	45 0.5×	20 0.5×	17	205
Hsien-Yu Meng United States	3	144 1.0×	120 1.1×	71 0.7×	107 1.2×	41 1.0×	4	223
Haoxi Ran China	3	116 0.8×	99 0.9×	54 0.6×	61 0.7×	40 0.9×	3	167
Matthias Niesner Germany	7	230 1.6×	135 1.2×	205 2.1×	80 0.9×	90 2.1×	8	361

Countries citing papers authored by Gusi Te

Since Specialization

Citations

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

Fields of papers citing papers by Gusi Te

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gusi Te

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

All Works

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2 of 2 papers shown

1.

Te, Gusi, Wei Hu, Yinglu Liu, Hailin Shi, & Tao Mei. (2021). AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing. IEEE Transactions on Image Processing. 30. 8236–8250. 23 indexed citations

2.

Te, Gusi, Wei Hu, Amin Zheng, & Zongming Guo. (2018). RGCNN. 746–754. 206 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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