Logistics
Where: Zoom
When: Tuesday/Thursday 2:00 PM - 3:15 PM (CT)
Forums: Campuswire
Instructor: Liangyan Gui
Office Hours: Monday 10:00 AM - 11:00 AM (CT) Zoom
TA: Shengcao Cao, Yunze Man
Office Hours: Wednesday 6:00 PM - 7:00 PM (CT) Zoom
Practice presentation: Zoom
Updates
Overview
Summary: Much of existing work in computer vision has focused on learning models of high accuracy. However, in real-world, resource-constrained scenarios, such as AR/VR, autonomous driving, and robots, being able to reduce latency and predict the environment?s dynamics is equally important. This advanced graduate course will cover foundation principles and recent progress of learning efficient and predictive models and their applications in domains such as vision, robotics, and NLP. We will investigate state-of-the-art approaches and a wide range of recent research topics, such as improving trade-off between accuracy and efficiency, predicting human motion/video/trajectory, anticipating action/event/intention, etc., in single- and multi-agent settings, and with multi-modal data. We will cover both the theoretical foundations and the techniques to build such practical systems, e.g., model compression, knowledge distillation, sequential modelling, predictive learning, multi-modal learning, etc.
People
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| Liangyan Gui | Shengcao Cao | Yunze Man |
| Instructor | TA | TA |
| lgui@illinois.edu | cao44@illinois.edu | yunzem2@illinois.edu |
Academic Integrity Policy
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The University of Illinois at Urbana-Champaign Student Code should also be considered as a part of this syllabus. Students should pay particular attention to Article 1, Part 4: Academic Integrity. Read the Code at the following URL: http://studentcode.illinois.edu/.
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Academic dishonesty may result in a failing grade. Every student is expected to review and abide by the Academic Integrity Policy: http://studentcode.illinois.edu/. Ignorance is not an excuse for any academic dishonesty. It is your responsibility to read this policy to avoid any misunderstanding. Do not hesitate to ask the instructor(s) if you are ever in doubt about what constitutes plagiarism, cheating, or any other breach of academic integrity.