Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Published on by Computer Vision Awards

Dr. Faisal Alamri | Object Detection for Security and Surveillance | Best Researcher Award

Chairperson of the Department of Computer Science and Information Technology | Jubail Industrial College (JIC) | Saudi Arabia

Dr. Faisal Alamri is an accomplished artificial intelligence researcher specializing in computer vision, machine learning, object detection, classification, segmentation, similarity search, adversarial perturbation, and zero-shot learning. He holds a Ph.D. in Computer Science with a focus on computer vision and machine learning from the University of Exeter, and completed his undergraduate and master’s degrees in computer systems engineering and networking. He currently serves as the Computer Science Department Chairperson at Jubail Industrial College, where he oversees academic and administrative activities and leads departmental initiatives. Previously, he worked as a machine learning engineer developing practical AI solutions, a postdoctoral research fellow, and a teaching assistant, and has also contributed as an online tutor and teaching volunteer. His research interests include developing innovative approaches for object detection, image analysis, and real-world AI applications. Dr. Alamri has been recognized for his achievements through multiple certifications and active participation in international conferences, workshops, and professional communities such as IEEE, Kaggle, NVIDIA, and MATLAB. He possesses strong technical skills in Python, MATLAB, C#, SPSS, AWS, Google Cloud ML Engine, and other platforms, and has completed various professional courses in deep learning, AI, cybersecurity, and digital analytics. His dedication to research, education, and community engagement reflects his commitment to advancing both science and society. He has a total of 49 citations, 7 documents, and an h-index of 5.

Profiles: Google Scholar | Scopus | ORCID | LinkedIn

Featured Publications

  1. Alamri, F., & Dutta, A. (2021). Multi-head self-attention via vision transformer for zero-shot learning. arXiv preprint arXiv:2108.00045.

  2. Alamri, F., & Pugeault, N. (2020). Improving object detection performance using scene contextual constraints. IEEE Transactions on Cognitive and Developmental Systems, 14(4), 1320–1330.

  3. Alamri, F., & Dutta, A. (2021). Implicit and explicit attention for zero-shot learning. In DAGM German Conference on Pattern Recognition (pp. 467–483).

  4. Alamri, F., & Dutta, A. (2023). Implicit and explicit attention mechanisms for zero-shot learning. Neurocomputing, 534, 55–66.

  5. Alamri, F., Kalkan, S., & Pugeault, N. (2021). Transformer-encoder detector module: Using context to improve robustness to adversarial attacks on object detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9577–9584). IEEE.

Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Published on by Computer Vision Awards

Prof. Xinrong Hu | Object Detection and Recognition | Women Researcher Award

Dean of Computer Science and Artificial Intelligence | Wuhan Textile University | China

Prof. Xinrong Hu is a distinguished researcher and academic leader in computer vision, natural language processing, virtual reality, and machine learning. She serves as Dean of the School of Computer and Artificial Intelligence at Wuhan Textile University and is a doctoral supervisor, leading an innovative research team at the Hubei Provincial Engineering Technology Research Center for Garment Informatization. She holds a Ph.D. and has extensive experience in guiding research projects, including over 30 funded initiatives, some with national and international significance. Her research interests focus on advancing artificial intelligence applications in real-world scenarios, combining theoretical innovation with practical solutions. She has authored more than 100 academic papers, edited six textbooks, translated a book, and holds 26 invention patents, demonstrating her strong research skills and contribution to knowledge dissemination. Prof. Hu has been recognized with multiple awards and honors, including provincial and ministerial-level scientific research awards, teaching achievement awards, and prestigious titles such as Hubei Provincial Distinguished Teacher and recipient of the Special Government Allowance from the State Council. Her professional engagement includes leadership in academic communities, mentorship of young researchers, and active participation in advancing the field of AI through both education and research initiatives. Her comprehensive expertise, innovative contributions, and dedication to fostering academic excellence make her a leading figure in her field. Her research impact is reflected in 1,044 citations, 209 documents, and an h-index of 16.

Profiles: Scopus | ResearchGate 

Featured Publications

  1. Hu, X., et al. (2025). CDPMF-DDA: Contrastive deep probabilistic matrix factorization for drug-disease association prediction. BMC Bioinformatics.

  2. Hu, X., et al. (2025). Source-free cross-modality medical image synthesis with diffusion priors. Journal of King Saud University – Computer and Information Sciences.

  3. Hu, X., et al. (2025). TADUFMA: Transformer-based adaptive denoising and unified feature modeling for multi-condition anomaly detection in computerized flat knitting machines. Measurement Science and Technology.

  4. Hu, X., et al. (2025). ViT-BF: Vision transformer with border-aware features for visual tracking. Visual Computer.

  5. Hu, X., et al. (2025). Adaptive debiasing learning for drug repositioning. Journal of Biomedical Informatics.