Peng Hu

4.7k total citations · 4 hit papers
88 papers, 3.0k citations indexed

About

Peng Hu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Peng Hu has authored 88 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Computer Vision and Pattern Recognition, 50 papers in Artificial Intelligence and 5 papers in Media Technology. Recurrent topics in Peng Hu's work include Advanced Image and Video Retrieval Techniques (39 papers), Domain Adaptation and Few-Shot Learning (22 papers) and Multimodal Machine Learning Applications (18 papers). Peng Hu is often cited by papers focused on Advanced Image and Video Retrieval Techniques (39 papers), Domain Adaptation and Few-Shot Learning (22 papers) and Multimodal Machine Learning Applications (18 papers). Peng Hu collaborates with scholars based in China, Singapore and United Kingdom. Peng Hu's co-authors include Dezhong Peng, Xi Peng, Liangli Zhen, Xu Wang, Yunfan Li, Joey Tianyi Zhou, Zitao Liu, Jiancheng Lv, Zhenyu Huang and Hongyuan Zhu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Peng Hu

83 papers receiving 2.9k citations

Hit Papers

Contrastive Clustering 2019 2026 2021 2023 2021 2019 2022 2022 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Contrastive Clustering
    2021 402 citations Yunfan Li, Peng Hu et al. profile →
  2. Deep Supervised Cross-Modal Retrieval
    2019 326 citations Peng Hu, Dezhong Peng et al. profile →
  3. All-In-One Image Restoration for Unknown Corruption
    2022 197 citations Boyun Li, Peng Hu et al. profile →
  4. Learning with Twin Noisy Labels for Visible-Infrared Person Re-Identification
    2022 160 citations Mouxing Yang, Zhenyu Huang et al. profile →

Peers

Peng Hu
Dacheng Tao Australia
Zuxuan Wu China
Hongyuan Zhu Singapore
Xia Li China
Yong Luo China
Liujuan Cao China
Zhenmin Tang China
Baoyuan Wu China
Weihua Ou China
Guopu Zhu China
Dacheng Tao Australia
Peng Hu
Citations per year, relative to Peng Hu Peng Hu (= 1×) peers Dacheng Tao

Countries citing papers authored by Peng Hu

Since Specialization
Citations

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

Fields of papers citing papers by Peng Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peng Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Peng Hu. A scholar is included among the top collaborators of Peng 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 Peng Hu. Peng Hu 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.
Li, Boyun, Yuanbiao Gou, Wenxin Wang, et al.. (2025). Relationship Quantification of Image Degradations. IEEE Transactions on Pattern Analysis and Machine Intelligence. 47(8). 7081–7092. 1 indexed citations
2.
Hu, Peng, et al.. (2025). Efficient and performant Transformer private inference with heterogeneous attention mechanisms. Applied Soft Computing. 176. 113150–113150. 1 indexed citations
3.
Li, Boyun, et al.. (2025). MaIR: A Locality-and Continuity-Preserving Mamba for Image Restoration. 7491–7501. 4 indexed citations
4.
Ren, Zhenwen, et al.. (2025). ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering with Noisy Correspondence. 30732–30741. 1 indexed citations
5.
Chen, Xiangru, et al.. (2024). Contrastive Learning with Transformer Initialization and Clustering Prior for Text Representation. Applied Soft Computing. 166. 112162–112162.
6.
Li, Xuedong, et al.. (2024). DifFilter: Defending Against Adversarial Perturbations With Diffusion Filter. IEEE Transactions on Information Forensics and Security. 19. 6779–6794. 4 indexed citations
7.
Sun, Yuan, et al.. (2024). Robust Multi-View Clustering With Noisy Correspondence. IEEE Transactions on Knowledge and Data Engineering. 36(12). 9150–9162. 28 indexed citations
8.
Sun, Yuan, et al.. (2024). RoMo: Robust Unsupervised Multimodal Learning With Noisy Pseudo Labels. IEEE Transactions on Image Processing. 33. 5086–5097. 4 indexed citations
9.
Yang, Mouxing, et al.. (2023). Semantic Invariant Multi-View Clustering With Fully Incomplete Information. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(4). 2139–2150. 20 indexed citations
10.
Li, Yunfan, Yijie Lin, Peng Hu, et al.. (2023). Single-Cell RNA-Seq Debiased Clustering via Batch Effect Disentanglement. IEEE Transactions on Neural Networks and Learning Systems. 35(8). 11371–11381. 6 indexed citations
11.
Hu, Peng, et al.. (2023). Learning relationship-preserving representation for multi-task adversarial attacks. Neurocomputing. 554. 126580–126580. 2 indexed citations
12.
Li, Haobin, Yunfan Li, Mouxing Yang, et al.. (2023). Incomplete Multi-view Clustering via Prototype-based Imputation. 3911–3919. 35 indexed citations
13.
Zhao, Ruxin, Yongli Wang, Gang Xiao, et al.. (2022). A method of path planning for unmanned aerial vehicle based on the hybrid of selfish herd optimizer and particle swarm optimizer. Applied Intelligence. 52(14). 16775–16798. 21 indexed citations
14.
Li, Boyun, et al.. (2022). Unsupervised Neural Rendering for Image Hazing. IEEE Transactions on Image Processing. 31. 3987–3996. 5 indexed citations
15.
Zhao, Ruxin, et al.. (2021). A selfish herd optimization algorithm based on the simplex method for clustering analysis. The Journal of Supercomputing. 77(8). 8840–8910. 9 indexed citations
16.
Wang, Yongli, et al.. (2020). Batch mode active learning via adaptive criteria weights. Applied Intelligence. 51(6). 3475–3489. 4 indexed citations
17.
Huang, Zhenyu, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, & Xi Peng. (2020). Partially View-aligned Clustering. Neural Information Processing Systems. 33. 2892–2902. 40 indexed citations
18.
Zhao, Ruxin, Yongli Wang, Chang Liu, et al.. (2020). Discrete selfish herd optimizer for solving graph coloring problem. Applied Intelligence. 50(5). 1633–1656. 4 indexed citations
19.
Gong, Ruihao, Xianglong Liu, Tianxiang Li, et al.. (2019). Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks. 4851–4860. 247 indexed citations
20.
Zhao, Ruxin, Yongli Wang, Peng Hu, et al.. (2019). Selfish herds optimization algorithm with orthogonal design and information update for training multi-layer perceptron neural network. Applied Intelligence. 49(6). 2339–2381. 17 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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