Qinghao Ye

1.9k total citations · 1 hit paper
34 papers, 993 citations indexed

About

Qinghao Ye is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Qinghao Ye has authored 34 papers receiving a total of 993 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 6 papers in Electrical and Electronic Engineering. Recurrent topics in Qinghao Ye's work include Multimodal Machine Learning Applications (12 papers), Topic Modeling (6 papers) and Natural Language Processing Techniques (6 papers). Qinghao Ye is often cited by papers focused on Multimodal Machine Learning Applications (12 papers), Topic Modeling (6 papers) and Natural Language Processing Techniques (6 papers). Qinghao Ye collaborates with scholars based in China, United States and United Kingdom. Qinghao Ye's co-authors include Guang Yang, Jun Xia, Ping Li, Xianghua Xu, Luming Zhang, Qi Bi, Ling Shao, Kun Qin, Gui-Song Xia and Fei Huang and has published in prestigious journals such as Journal of Applied Physics, Scientific Reports and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Qinghao Ye

29 papers receiving 951 citations

Hit Papers

Unbox the black-box for the medical explainable AI via mu... 2021 2026 2022 2024 2021 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qinghao Ye China 14 406 343 223 148 87 34 993
Fahad Shamshad Pakistan 7 280 0.7× 301 0.9× 312 1.4× 34 0.2× 102 1.2× 16 782
Shih-Cheng Huang United States 10 535 1.3× 184 0.5× 589 2.6× 177 1.2× 71 0.8× 22 1.3k
Melissa Berthelot United Kingdom 6 511 1.3× 203 0.6× 346 1.6× 99 0.7× 104 1.2× 11 1.3k
Laura Igual Spain 17 229 0.6× 601 1.8× 243 1.1× 44 0.3× 47 0.5× 41 1.3k
Ali Mottaghi United States 4 235 0.6× 173 0.5× 229 1.0× 103 0.7× 42 0.5× 5 743
Muhammed Yıldırım Türkiye 18 448 1.1× 413 1.2× 411 1.8× 37 0.3× 364 4.2× 86 1.2k
Feng Yang China 18 502 1.2× 546 1.6× 1.2k 5.2× 46 0.3× 77 0.9× 81 2.0k
Xiaoxia Yin Australia 19 247 0.6× 170 0.5× 388 1.7× 26 0.2× 50 0.6× 80 1.2k
Ruogu Fang United States 18 273 0.7× 590 1.7× 662 3.0× 49 0.3× 129 1.5× 71 1.6k
Veronika Cheplygina Netherlands 14 593 1.5× 464 1.4× 381 1.7× 92 0.6× 60 0.7× 24 1.3k

Countries citing papers authored by Qinghao Ye

Since Specialization
Citations

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

Fields of papers citing papers by Qinghao Ye

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qinghao Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Qinghao Ye. A scholar is included among the top collaborators of Qinghao Ye 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 Qinghao Ye. Qinghao Ye 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.
Wang, Xiyao, et al.. (2025). LLLaVA-Critic: Learning to Evaluate Multimodal Models. 13618–13628. 1 indexed citations
2.
Ye, Wei, Haiyang Xu, Qinghao Ye, et al.. (2024). TiMix: Text-Aware Image Mixing for Effective Vision-Language Pre-training. Proceedings of the AAAI Conference on Artificial Intelligence. 38(3). 2489–2497.
3.
Ye, Jiabo, Ming Yan, Haiyang Xu, et al.. (2024). UniQRNet: Unifying Referring Expression Grounding and Segmentation with QRNet. ACM Transactions on Multimedia Computing Communications and Applications. 20(8). 1–28. 3 indexed citations
4.
Yan, Ming, et al.. (2024). Hallucination Augmented Contrastive Learning for Multimodal Large Language Model. 27026–27036. 18 indexed citations
5.
Ye, Qinghao, Haiyang Xu, Jiabo Ye, et al.. (2024). mPLUG-OwI2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration. 13040–13051. 44 indexed citations
6.
Li, Ping, et al.. (2023). Truncated attention-aware proposal networks with multi-scale dilation for temporal action detection. Pattern Recognition. 142. 109684–109684. 5 indexed citations
7.
Yang, Xu, Jiawei Peng, Qinghao Ye, et al.. (2023). Transforming Visual Scene Graphs to Image Captions. 12427–12440. 12 indexed citations
8.
Xu, Haiyang, Wei Ye, Qinghao Ye, et al.. (2023). COPA : Efficient Vision-Language Pre-training through Collaborative Object- and Patch-Text Alignment. 4480–4491. 7 indexed citations
9.
Xu, Yang, Haiyang Xu, Hanwang Zhang, et al.. (2023). Learning Trajectory-Word Alignments for Video-Language Tasks. 33. 2504–2514. 1 indexed citations
10.
Ye, Jiabo, Anwen Hu, Haiyang Xu, et al.. (2023). UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model. 2841–2858. 22 indexed citations
11.
Ye, Qinghao, et al.. (2022). Exploring Global Diversity and Local Context for Video Summarization. IEEE Access. 10. 43611–43622. 6 indexed citations
12.
Ye, Qinghao, Jun Xia, & Guang Yang. (2021). Explainable AI for COVID-19 CT Classifiers: An Initial Comparison Study. 521–526. 67 indexed citations
13.
Ye, Qinghao, Yuan Gao, Weiping Ding, et al.. (2021). Robust weakly supervised learning for COVID-19 recognition using multi-center CT images. Applied Soft Computing. 116. 108291–108291. 32 indexed citations
14.
Ma, Huijing, Qinghao Ye, Weiping Ding, et al.. (2021). Can Clinical Symptoms and Laboratory Results Predict CT Abnormality? Initial Findings Using Novel Machine Learning Techniques in Children With COVID-19 Infections. Frontiers in Medicine. 8. 699984–699984. 9 indexed citations
15.
Yang, Guang, Qinghao Ye, & Jun Xia. (2021). Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Information Fusion. 77. 29–52. 457 indexed citations breakdown →
16.
Ye, Qinghao, Yinghui Jiang, Minhao Wang, et al.. (2020). Systematic and Comprehensive Automated Ventricle Segmentation on Ventricle Images of the Elderly Patients: A Retrospective Study. Frontiers in Aging Neuroscience. 12. 618538–618538. 24 indexed citations
17.
Zeng, Yang, Hong Liu, Qinghao Ye, & Wen Zhong Shen. (2014). Enhanced carrier extraction of a-Si/c-Si solar cells by nanopillar-induced optical modulation. Nanotechnology. 25(13). 135202–135202.
18.
Zeng, Yang, Qinghao Ye, & Wenzhong Shen. (2014). Design principles for single standing nanowire solar cells: going beyond the planar efficiency limits. Scientific Reports. 4(1). 4915–4915. 16 indexed citations
19.
Wang, Jian‐Qiang, et al.. (2012). Investigation of an a-Si/c-Si interface on a c-Si(P) substrate by simulation. Journal of Semiconductors. 33(3). 33001–33001. 5 indexed citations
20.
Ye, Qinghao, Chris Xu, Xiang Liu, et al.. (2002). Dispersion measurement of tapered air-silica microstructure fiber by white-light interferometry. Applied Optics. 41(22). 4467–4467. 34 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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