Kun Chen | Machine Learning for Computer Vision | Research Excellence Award

Mr. Kun Chen | Machine Learning for Computer Vision | Research Excellence Award

East China Jiaotong University | China

Mr. Kun Chen is a postgraduate researcher at East China Jiaotong University, specializing in machine learning and data mining. His research focuses on clustering analysis and semi-supervised learning, contributing to advancing intelligent data-driven systems. He co-authored the article A Novel Semi-Supervised Clustering Algorithm Based on Ridge Regression with Optimal Scaling, published in Neurocomputing, demonstrating strong analytical and methodological innovation. Despite being in the early stage of his academic career, he shows promising potential through international collaboration and impactful research contributions aimed at improving data interpretation and decision-making across scientific and engineering domains.

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Tao Chen | Object Detection and Recognition | Research Excellence Award

Dr. Tao Chen | Object Detection and Recognition | Research Excellence Award

Professor | Fudan University | China

Dr. Tao Chen is a leading researcher at Fudan University, specializing in deep learning and computer vision, with a focus on human motion understanding, 3D shape generation, and semantic segmentation. He has contributed to over 249 high-impact publications in top-tier venues including CVPR, NeurIPS, and IEEE Transactions, accumulating more than 6294 citations. His work integrates advanced neural architectures, motion diffusion, and cross-domain adaptation techniques, often in collaboration with international researchers such as G. Yu and W. Liu. Dr. Chen’s research has significant societal impact, advancing intelligent systems for medical imaging, autonomous perception, and interactive 3D applications, bridging fundamental AI research with practical real-world solutions.

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6294

Documents
249

h-index
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Featured Publications


Executing your commands via motion diffusion in latent space.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . (2023). Cited By : 580

TopFormer: Token pyramid transformer for mobile semantic segmentation.

-In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2022). Cited By: 388

b‑DARTS: Beta‑decay regularization for differentiable architecture search.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2022). Cited By: 194

LL3DA: Visual interactive instruction tuning for omni‑3D understanding, reasoning, and planning.

– In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). (2024). Cited By: 178

Steven Sheng-Uei Guan | Machine Learning for Computer Vision | Research Excellence Award

Prof. Dr. Steven Sheng-Uei Guan | Machine Learning for Computer Vision | Research Excellence Award

Professor | Xi’an Jiaotong-Liverpool University | Australia

Prof. Dr. Steven Sheng Uei Guan is an accomplished researcher at Xi’an Jiaotong-Liverpool University, China, with a Scopus h-index of 25, over 244 publications, and more than 2,362 citations. His research expertise spans human–object interaction detection, graph neural networks, continual learning, human–robot interaction, blockchain-enabled data trading, and intelligent healthcare systems. Dr. Guan has collaborated with over 200 international co-authors, reflecting his strong global research network. His work contributes significantly to advancing artificial intelligence for real-world perception, secure data sharing, and socially beneficial intelligent systems, impacting domains such as robotics, medical informatics, and computational social systems.

Citation Metrics (Scopus)

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2362

Documents
244

h-index
25

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Featured Publications


Encyclopedia of information science and technology.

– IGI Global. (2018). Cited By : 454

Parameter estimation of photovoltaic models via cuckoo search.

-Parameter estimation of photovoltaic models via cuckoo search. (2013). Cited By: 303

An incremental approach to genetic-algorithms-based classification.

-Multimedia Tools and Applications. (2005). Cited By: 124

Investigation of neural networks for function approximation.

– Procedia Computer Science. (2013). Cited By: 111

Riadh Harizi | Deep Learning For Computer Vision | Research Excellence Award

Dr. Riadh Harizi | Deep Learning For Computer Vision | Research Excellence Award

Teacher | Ecole Nationale d’Ingénieurs de Sfax | Tunisia

Dr. Riadh Harizi is a researcher at the École Nationale d’Ingénieurs de Sfax, Tunisia, with expertise in Machine Learning, Artificial Intelligence, Computer Vision, Deep Learning, and Data Science. He has authored 5 research outputs, receiving 33 citations across 25 citing documents and achieving an h-index of 3. His work spans scene text understanding, reinforcement learning, and AI-driven educational analytics, with publications in Applied Soft Computing, Multimedia Tools and Applications, and leading international conferences. He has collaborated with interdisciplinary teams and contributed an open Latin and Arabic scene character dataset to IEEE Dataport, supporting reproducible research and societal impact in education and intelligent visual systems.

 

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33

Documents
5

h-index
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Deep-learning based end-to-end system for text reading in the wild.

-Multimedia Tools and Applications. (2022) Cited By: 10

SIFT-ResNet synergy for accurate scene word detection in complex scenarios.

– In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (ICAART) . (2024). Cited By: 3

Zeng Gao | Applications of Computer Vision | Research Excellence Award

Dr. Zeng Gao | Applications of Computer Vision | Research Excellence Award

Lecturer | Henan University of Technology | China 

Dr. Zeng Gao is a researcher at Henan University of Technology specializing in machine learning, image processing, and visual tracking. His work focuses on intelligent optimization–driven visual tracking and motion analysis, with influential contributions to abrupt and long-term tracking. He has published over 12 peer-reviewed papers in leading international journals and conferences, including IEEE Access, Expert Systems with Applications, Applied Soft Computing, Digital Signal Processing, and PRCV, accumulating 98 citations. He has participated in two National Natural Science Foundation of China projects and holds three granted invention patents. Dr. Gao actively collaborates with domestic and international institutions, serves as a reviewer for journals such as ACM TOMM and Digital Signal Processing, and contributes to advancing intelligent perception technologies with real-world societal impact.

 

Citation Metrics (Scopus)

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Citations
98

Documents
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Visual tracking with levy flight grasshopper optimization algorithm.

– Pattern Recognition and Computer Vision: Second Chinese Conference, PRCV  (2019). Cited By : 19

Divya Nimma | Applications of Computer Vision | Women Researcher Award

Assist. Prof. Dr. Divya Nimma | Applications of Computer Vision | Women Researcher Award

Assistant Professor | Arkansas Tech University | United States

Dr. Divya Nimma is an accomplished researcher and Assistant Professor at Arkansas Tech University, specializing in Computer Vision, Artificial Intelligence, Image Processing, and Machine Learning. With a strong interdisciplinary footprint, she has contributed extensively to domains spanning environmental monitoring, healthcare analytics, intelligent transportation cybersecurity and immersive technologies. She has published 46 scholarly works and accumulated over 326 citations, with an h-index of 10 and i10-index of 10, underscoring her growing global research influence.Dr. Nimma’s research portfolio reflects a commitment to developing intelligent systems for real-world impact. Her notable contributions include climate-responsive modeling of freshwater ecosystems remote sensing–based marine life assessment for food security transformer-driven object detection , and advanced deep learning frameworks for image forensics and semantic segmentation. She has led and co-authored high-impact studies published in Scientific Reports IEEE Transactions Alexandria Engineering Journal Desalination and Water Treatment Remote Sensing in Earth Systems Sciences and other reputed journals.Her collaborative research spans international teams across the United States  Europe the Middle East  and Asia. Significant works include attention-based models for real-time surveillance explainable AI pipelines for fingerprint recognition IoT-enabled energy management for EV charging predictive maintenance in Industry 4.0 and multisource wearable data analytics for human activity recognition.Dr. Nimma has also made influential contributions to biomedical informatics including cancer detection using optimized deep learning osteoporosis classification and non-invasive brain stimulation–based sleep stage modeling. Additionally her research extends to precision agriculture integrating drone imagery AI and consumer electronics to enhance crop optimization and sustainability.Committed to societal and technological advancement Dr. Nimma’s work demonstrates a unique synthesis of deep learning innovation domain-driven applications and cross-disciplinary collaboration positioning her as a rising scholar and impactful global contributor in modern AI-driven intelligent systems.

Profiles:  Scopus | ORCID | Googlescholar

Featured Publications

1. Nimma, D., Devi, O. R., Laishram, B., Ramesh, J. V. N., Boddupalli, S., Ayyasamy, R., et al. (2025). Implications of climate change on freshwater ecosystems and their biodiversity. Desalination and Water Treatment, 321, 100889. Cited By : 42

2. Srikanth, G., Nimma, D., Lalitha, R. V. S., Jangir, P., Kumari, N. V. S., & Arpita. (2025). Food security-based marine life ecosystem for polar region conditioning: Remote sensing analysis with machine learning model. Remote Sensing in Earth Systems Sciences, 8(1), 65–73. Cited By : 36

3. Nimma, D., Nimma, R., Rajendar, & Uddagiri. (2024). Image processing in augmented reality (AR) and virtual reality (VR). International Journal on Recent and Innovation Trends in Computing and Communication. Cited By : 27

4. Nimma, D., & Zhou, Z. (2024). IntelPVT: Intelligent patch-based pyramid vision transformers for object detection and classification. International Journal of Machine Learning and Cybernetics, 15(5), 1767–1778. Cited By : 25

5. Nimma, D., Nimma, R., & Uddagiri, A. (2024). Advanced image forensics: Detecting and reconstructing manipulated images with deep learning. International Journal of Intelligent Systems and Applications in Engineering.
Cited By : 24

Dr. Divya Nimma’s research advances intelligent vision systems that enhance environmental sustainability, healthcare diagnostics, and smart transportation. Her work integrates AI with real-world applications, driving scientific innovation that strengthens societal resilience and global technological progress.