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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.
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Advancing Visual Intelligence via Neural System Design

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KAUST

Building intelligent visual systems is essential for the next generation of artificial intelligence systems. It is a fundamental tool for many disciplines and beneficial to various potential applications such as autonomous driving, robotics, surveillance, augmented reality, to name a few. An accurate and efficient intelligent visual system has a deep understanding of the scene, objects, and humans. It can automatically understand the surrounding scenes. In general, 2D images and 3D point clouds are the two most common data representations in our daily life. Designing powerful image understanding and point cloud processing systems are two pillars of visual intelligence, enabling the artificial intelligence systems to understand and interact with the current status of the environment automatically. In this talk, I will first present our efforts in designing modern neural systems for 2D image understanding, including high-accuracy and high-efficiency semantic parsing structures, and unified panoptic parsing architecture. Then, we go one step further to design neural systems for processing complex 3D scenes, including semantic-level and instance-level understanding. Further, we show our latest works for unified 2D-3D reasoning frameworks, which are fully based on self-attention mechanisms. In the end, the challenges, up-to-date progress, and promising future directions for building advanced intelligent visual systems will be discussed.
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