CS 4476Β
Computer Vision
(Spring 2024)
Course Description
This is an introductory undergraduate course to computer vision which strikes the balance of classic vision topics such as light and color, image filtering, feature detection, transforms, stereo vision etc., and modern vision topics such as deep learning based visual recognition, generative models, transformers and diffusion models. The focus of the course is to develop intuitions and mathematics of the methods in the lectures, and then to learn about the difference between theory and practice in the projects.
Lectures
Schedule is tentative and subject to change.
Links to recent/next lecture slides PDF and lecture recordings are shared through Canvas.
Textbook is not required; however, for those seeking an accessible textbook to accompany the course, you can check out the resources here. (Szeliski 2nd ed)
We use Ed Dicussion for more direct online communication.
Assignments, Exams and Grading
Five Assignments with 14 pts each, total 70 pts.
Assignments are released through Canvas/ GitHub, and submitted through GradeScope.Β
You have five "late days" for the whole course without penalty. You will lose 10% each day for late projects, and late submissions will not be accepted after 1 week.
There are extra credit points for several projects and the total score for those can be up to 110% of 14pts.
Two in-class Exams with 15 pts each, total 30 pts.
Extra credit for participation can be claimed, up to 3pts.
Final Grades: A: 90%+, B: 80%+, C: 70%+, D: 60%+, F<60%.
Teaching Assistants
TA Office Hours:Β Β
2hrs/weekday, see table below.
Extra office hours are possible if the time doesn't work for you, contact your dedicated TA mentor as needed. (mentor list distributed through Canvas)
Location:Β
KLAUS 2nd Floor Atrium.Β
TA Contacts:
Useful Links:
https://catalog.gatech.edu/policies/student-absence-regulations/
Acknowledgement:Β
Course materials are built on top of contents from great colleagues including but not limited to: Judy Hoffman, James Hays, Svetlana Lazbnik, Fei-Fei Li, Kristen Grauman, Devi Parikh, Derek Hoiem, Bill Freeman, Alexei Effros and many more.