A graduate-level course in computer vision, with an emphasis on high-level recognition tasks. We will read an eclectic mix of classic and contemporary papers on a wide-range of topics. The course structure will combine lectures, in-class discussions, assignments, and a course project. The goal of this course is to:
While there are no formal prerequisites for this course, familiarity with introductory courses in computer vision (CMSC426 or similar) and machine/deep learning learning (CMSC422 or similar) is assumed. If you have not taken courses covering this material, consult with the instructor. Note that a basic knowledge of linear algebra, probability, and calculus is required.
CSI 2117
Tuesday, Thursday 11:00am - 12:15pm
Abhinav Shrivastava
4238 IRB
abhinav@cs.umd.edu
Office hours: As required & by appointment.
Matthew Gwilliam
mgwillia@umd.edu
Office hours: TBD.
Mara Levy
mlevy@umd.edu
Office hours: TBD.
Saksham Suri
sakshams@umd.edu
Office hours: TBD.
Piazza
ELMS-Canvas
Web Accessibility
CMSC828I Fall 2021
CMSC828I Fall 2020
CMSC828I Fall 2019
CMSC828I Fall 2018
For a comprehensive review of Computer Vision, please refer to "Computer Vision: Algorithms and Applications by Richard Szeliski. The book is available for free online or available for purchase.
Note that academic dishonesty includes not only cheating, fabrication, and plagiarism, but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work. In short, all submitted work must be your own. Cases of academic dishonesty will be pursued to the fullest extent possible as stipulated by the Office of Student Conduct. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit http://www.shc.umd.edu.
Any student who needs to be excused for an absence from a single lecture, recitation, or lab due to a medically necessitated absence shall:
Any student who needs to be excused for a Major Scheduled Grading Event, must provide written documentation of the illness from the Health Center or from an outside health care provider. This documentation must verify dates of treatment and indicate the time frame that the student was unable to meet academic responsibilities. No diagnostic information shall be given. The Major Scheduled Grading Events for this course include midterm and final exam. For class presentations, the instructor will help the student swap their presentation slot with other students.
It is also the student's responsibility to inform the instructor of any intended absences from exams and class presentations for religious observances in advance. Notice should be provided as soon as possible, but no later than the Monday prior to the the midterm exam, the class presentation date, and the final exam.
Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide a letter of accommodation from the Office of Disability Support Services within the first three weeks of the semester.
You can find the university’s course policies here.