Guang Dai

124 total papers · 809 total citations
55 papers, 501 citations indexed

About

Guang Dai is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Guang Dai has authored 55 papers receiving a total of 501 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Computer Vision and Pattern Recognition, 20 papers in Artificial Intelligence and 8 papers in Computational Mechanics. Recurrent topics in Guang Dai's work include Face and Expression Recognition (23 papers), Sparse and Compressive Sensing Techniques (7 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Guang Dai is often cited by papers focused on Face and Expression Recognition (23 papers), Sparse and Compressive Sensing Techniques (7 papers) and Domain Adaptation and Few-Shot Learning (5 papers). Guang Dai collaborates with scholars based in China, Hong Kong and United States. Guang Dai's co-authors include Dit–Yan Yeung, Michael I. Jordan, Zhihua Zhang, Zhihua Zhang, Dit‐Yan Yeung, Yuntao Qian, Jingdong Wang, Congfu Xu, Changle Zhou and Hui Qian and has published in prestigious journals such as IEEE Transactions on Image Processing, Pattern Recognition and International Journal of Computer Vision.

In The Last Decade

Guang Dai

49 papers receiving 473 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Guang Dai	311	174	69	60	42	55	501
Zhengming Ma	289 0.9×	219 1.3×	45 0.7×	75 1.3×	26 0.6×	60	509
Lei Zhang	378 1.2×	156 0.9×	68 1.0×	99 1.6×	81 1.9×	31	558
Danyang Wu	393 1.3×	355 2.0×	46 0.7×	28 0.5×	21 0.5×	42	588
Patrick Gallinari	166 0.5×	293 1.7×	33 0.5×	57 0.9×	10 0.2×	49	588
Wei Yan	193 0.6×	119 0.7×	48 0.7×	49 0.8×	9 0.2×	55	602
Xizhan Gao	249 0.8×	143 0.8×	21 0.3×	32 0.5×	8 0.2×	54	465
Xujun Peng	476 1.5×	82 0.5×	52 0.8×	40 0.7×	20 0.5×	44	560
Xuan Li	320 1.0×	159 0.9×	19 0.3×	80 1.3×	32 0.8×	58	616
Akisato Kimura	379 1.2×	183 1.1×	12 0.2×	99 1.6×	21 0.5×	67	597
Ping Deng	192 0.6×	204 1.2×	27 0.4×	56 0.9×	8 0.2×	42	614

Countries citing papers authored by Guang Dai

Since Specialization

Citations

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

Fields of papers citing papers by Guang Dai

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guang Dai

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

All Works

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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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