Logistics
Where: Zoom
When: Monday/Wednesday/ 9:30 AM - 10:45 AM (CST)
Forums: Piazza
Instructor: Yuxiong Wang
Office Hours: Monday 10:45 AM - 12:00 PM (CST) Zoom
TA: Amnon Attali
Office Hours: Wednesday 10:45 AM - 12:00 PM (CST) Zoom
Practice presentation: Zoom
Updates
Overview
Summary: This course will cover advanced research topics in computer vision, with emphasis on recognition tasks and deep learning. Building on the introductory materials in CS 543 (Computer Vision), this course will prepare graduate students in both the theoretical foundations of computer vision and the state-of-the-art approaches to building real-world computer vision systems. We will investigate data sources, model architectures, and learning algorithms that are useful for understanding and manipulating visual data. This course will start by focusing on representation and reasoning for large amounts of data (images, videos, 3D point clouds, associated tags, text, gps-locations, etc). We will then in particular discuss recent efforts towards visual learning and reasoning with less human supervision. Students will be required to read, present, critique, and discuss research papers and perform a related research project. By the end of the course, students will be able to understand and implement the state-of-the-art algorithms as well as identify important open questions and future research directions. Students will be also ready to conduct research in computer vision and its relevant domains such as robotics.
Prerequisites:
Awards: At the end of the course, we will vote for:
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Best Participation (Piazza posts & In-class discussion)
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Best Presentation
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Best Project
People
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| Yuxiong Wang | Amnon Attali |
| Instructor | TA |
| yxw@illinois.edu | aattali2@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.
Credits and Acknowledgment
I gratefully thank Abhinav Gupta, Lana Lazebnik, Alyosha Efros, James Hays, and Judy Hoffman for borrowing much of their course design and slides.