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Research at the Image and Video Understanding Lab (IVUL) - Graduate Seminar - ECE

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KAUST

In this talk, I will give an overview of research done in the Image and Video Understanding Lab (IVUL) at KAUST. At IVUL, we work on topics that are important to the computer vision (CV) and machine learning (ML) communities, with emphasis on three research themes: Theme 1 (Video Understanding): We aim to extract meaningful semantic information from large-scale video data by tackling research problems such as object tracking, activity detection, moment retrieval, and language grounding in video. Theme 2 (Visual Computing for Automated Navigation): We develop methodology to enable more accurate, reliable, and robust perception of the visual world for automated navigation applications (e.g. self-driving cars and UAVs). In this theme, we tackle research problems such as object tracking, segmentation, and detection in 3D data, as well as transfer learning from simulation (sim2real). Theme 3 (Fundamentals/Foundations): In this theme, we tackle fundamental research problems in CV and ML that transcend specific applications with focus on deep network theory/analysis (e.g. robustness, certification, and interpretability) and structured optimization methods for large-scale CV/ML problems. Throughout the talk, I will highlight some of the interesting projects at IVUL to encourage students to get interested in the research field.

Visualization helps science to see the unexpected

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Advances in how science is presented means that visual tools can inspire research, as well as make its results accessible to the world.