🚀 CVML-KU

Computer Vision and Machine Intelligence Lab

Khalifa University

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🧭 About

The CVML Lab at Khalifa University advances robust computer vision and machine learning, developing intelligent visual systems for real-world applications in security and surveillance, biometrics, medical imaging, remote sensing, and autonomous systems.

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📚 Research Areas & Featured Publications

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🔍 Surveillance and Inspection

Researching intelligent visual and audio analytics for security, environmental monitoring, and automated inspection across complex real-world environments. This includes X-ray/CT threat detection, crowd tracking, flare analysis, and robust speech-based identification in noisy public spaces.

📄 Featured Works

📘 Title / Paper	📝 Description	🗂️ Repository	🏅 Venue	🎬 Demo
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection	A domain-aware vision-language model to support multimodal X-ray security tasks (Grounding, VQA, and scene understanding).	STING-BEE	CVPR'25-Highlight	Demo
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection	A domain-aware vision-language model to support multimodal X-ray security tasks (Grounding, VQA, and scene understanding).	Repo	CVPR'25-Highlight	Demo
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection	A domain-aware vision-language model to support multimodal X-ray security tasks (Grounding, VQA, and scene understanding).	Repo	CVPR'25-Highlight	Demo

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🩺 Medical Imaging

We develop advanced computer vision and machine learning methods for the analysis of diverse medical imaging modalities, including MRI, CT, X-ray, fundus, and digital pathology. Our work aims to build robust computational frameworks that enhance diagnostic accuracy, support quantitative interpretation, and improve clinical decision-making across healthcare applications.

📄 Featured Works

📘 Title / Paper	📝 Description	🗂️ Repository	🏅 Venue	🎬 Demo
DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation	A dynamic uncertainty-aware semi-supervised segmentation framework for robust learning under class imbalance.	DyCON	CVPR'25	https://youtube.com/dummy-demo
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection	A domain-aware vision-language model to support multimodal X-ray security tasks (Grounding, VQA, and scene understanding).	Repo	CVPR'25	Demo
STING-BEE: Towards Vision-Language Model for Real-World X-ray Baggage Security Inspection	A domain-aware vision-language model to support multimodal X-ray security tasks (Grounding, VQA, and scene understanding).	Repo	CVPR'25	Demo

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🗂️ Other Repositories

Tools, utilities, sandbox code, and projects.

🗂️ Repository	📝 Description	🔗 Link
STING-BEE Tools	Utility tools and helper scripts for multimodal X-ray security research.	https://github.com/dummy-repo
STING-BEE Sandbox	Experimental code and prototypes for vision-language modeling in X-ray security.	https://github.com/dummy-repo

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📄 Publications

You may link to:

• Google Scholar
• ResearchGate
• KU faculty profile
• Lab publications page

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📬 Contact

Website: (add link)
Email: (lab or PI email)

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🙏 Acknowledgements

(Optional section for grants, funding bodies, or collaborators.)

Overview

CVML-KU Organization Profile