Juan Hu

525 total citations
10 papers, 350 citations indexed

About

Juan Hu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Urban Studies. According to data from OpenAlex, Juan Hu has authored 10 papers receiving a total of 350 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Urban Studies. Recurrent topics in Juan Hu's work include Generative Adversarial Networks and Image Synthesis (4 papers), Digital Media Forensic Detection (3 papers) and Advanced Image Processing Techniques (2 papers). Juan Hu is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (4 papers), Digital Media Forensic Detection (3 papers) and Advanced Image Processing Techniques (2 papers). Juan Hu collaborates with scholars based in China, Hong Kong and Canada. Juan Hu's co-authors include Xin Liao, Zheng Qin, Wei Wang, Tianyi Wang, Yumei Wang, Jiliu Zhou, Fangnian Lang, Jinwen Liang, Wenbo Zhou and Xuanxia Yao and has published in prestigious journals such as IEEE Access, Frontiers in Psychology and IEEE Transactions on Circuits and Systems for Video Technology.

In The Last Decade

Juan Hu

8 papers receiving 336 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Juan Hu China	5	280	83	37	33	17	10	350
Daniel Berjón Spain	10	219 0.8×	39 0.5×	33 0.9×	35 1.1×	11 0.6×	33	328
Qidong Huang China	7	171 0.6×	141 1.7×	19 0.5×	36 1.1×	17 1.0×	16	294
Edgar Riba Austria	3	402 1.4×	88 1.1×	34 0.9×	10 0.3×	7 0.4×	3	484
Giorgos Tolias Czechia	13	627 2.2×	93 1.1×	63 1.7×	22 0.7×	5 0.3×	36	668
Jiaqi Lin China	8	145 0.5×	108 1.3×	20 0.5×	10 0.3×	13 0.8×	26	256
Anirban Roy United States	8	290 1.0×	110 1.3×	109 2.9×	19 0.6×	11 0.6×	24	380
Minoru Maruyama Japan	9	231 0.8×	47 0.6×	55 1.5×	41 1.2×	7 0.4×	44	303
Qi Cai China	8	314 1.1×	191 2.3×	15 0.4×	56 1.7×	7 0.4×	16	380
Qi-Zhi Cai United States	5	212 0.8×	91 1.1×	14 0.4×	21 0.6×	13 0.8×	6	287
Gengshen Wu China	8	389 1.4×	85 1.0×	34 0.9×	17 0.5×	4 0.2×	20	446

Countries citing papers authored by Juan Hu

Since Specialization

Citations

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

Fields of papers citing papers by Juan Hu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Juan Hu

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Hu, Juan, Jinwen Liang, Zheng Qin, et al.. (2024). ADA-FInfer: Inferring Face Representations From Adaptive Select Frames for High-Visual-Quality Deepfake Detection. IEEE Transactions on Dependable and Secure Computing. 22(3). 3011–3027. 3 indexed citations

2.

Hu, Juan, et al.. (2024). Compound Facial Expression Recognition Based on Graph Convolutional Multi-Label Learning. 433–437.

3.

Liao, Xin, et al.. (2023). FAMM: Facial Muscle Motions for Detecting Compressed Deepfake Videos Over Social Networks. IEEE Transactions on Circuits and Systems for Video Technology. 33(12). 7236–7251. 71 indexed citations

4.

Wang, Wei & Juan Hu. (2022). Extraction of PE Online Teaching Resources With Positive Psychology Based on Advanced Intelligence Algorithm. Frontiers in Psychology. 13. 948721–948721. 1 indexed citations

5.

Hu, Juan, Xin Liao, Jinwen Liang, Wenbo Zhou, & Zheng Qin. (2022). FInfer: Frame Inference-Based Deepfake Detection for High-Visual-Quality Videos. Proceedings of the AAAI Conference on Artificial Intelligence. 36(1). 951–959. 59 indexed citations

6.

Hu, Juan, Xin Liao, Wei Wang, & Zheng Qin. (2021). Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network. IEEE Transactions on Circuits and Systems for Video Technology. 32(3). 1089–1102. 139 indexed citations

7.

Kong, Xu, et al.. (2019). The gold metallogenic prognosis based on the weighting method of evidence in the middle Xuefeng mountain area. 41(6). 832–842. 2 indexed citations

8.

Yao, Xuanxia, et al.. (2019). Using Deep Learning in Semantic Classification for Point Cloud Data. IEEE Access. 7. 37121–37130. 24 indexed citations

9.

Zhou, Yipeng, Juan Hu, & Junping Du. (2012). Cross-media topic analysis and information retrieval. 4. 1211–1215.

10.

Zhou, Jiliu, et al.. (2011). Edge detection of colour image based on quaternion fractional differential. IET Image Processing. 5(3). 261–272. 51 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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