EECS 442: Computer Vision

EECS 442 is an advanced undergraduate-level computer vision class. Class topics include low-level vision, object recognition, motion, 3D reconstruction, basic signal processing, and deep learning. We'll also touch on very recent advances, including image synthesis, self-supervised learning, and embodied perception.

Lectures:

Discussion section:
This class has two discussion sections, which both use the same Zoom link as the lecture:

Some weeks, we'll host tutorials during these sections, where GSIs will cover a topic in depth (e.g. a PyTorch guide). These tutorials appear in the schedule. Attendance to these tutorials is optional, and recordings will be posted online. Other weeks, the discussion section will function as additional office hours and project discussion.

Prerequisites:

Q&A: This course has a Piazza forum, where you can ask public questions. If you cannot make your post public (e.g., due to revealing problem set solutions), please mark your post private, or come to office hours. Please note, however, that the course staff cannot provide help debugging code, and there is no guarantee that they'll be able to answer last-minute homework questions before the deadline. We also appreciate it when you respond to questions from other students! If you have an important question that you would prefer to discuss over email, you may email the course staff (eecs442-fa20-staff@umich.edu), or you can contact the instructor by email directly.

Homework: There will be homework assignments approximately every week. All programming assignments are to be completed in Python, using the starter code that we provide. Assignments will always be due at midnight (11:59pm) on the due date. The assignments will all be worth approximately equal amounts. Written problems will usually be submitted to Gradescope. You may be asked to annotate your pdf (e.g. by selecting your solution to each problem).

Final project: In lieu of a final exam, we'll have final project. This project will be completed in small groups during the last weeks of the class. The direction for this project is open-ended: you can either choose from a list of project ideas that we distribute, or you can propose a topic of your own. A short project proposal will be due approximately halfway through the course. During the final exam period, you'll turn in a final report and give a short presentation. You may use an ongoing research work for your final project, as long it meets the requirements.

Textbook: There are no required textbooks to purchase. As an experiment, we'll be using a new draft version of the online book:

Acknowledgements: This course draws heavily from MIT's 6.869: Advances in Computer Vision by Antonio Torralba, William Freeman, and Phillip Isola. It also includes lecture slides from other researchers, including Svetlana Lazebnik, Alexei Efros, David Fouhey, and Noah Snavely (please see acknowledgments in the lecture slides).

Late policy: You'll have 7 9 late days to use over the course of the semester. Each time you use one, you may submit a homework assignment one day late without penalty. You are allowed to use multiple late days on a single assignment. For example, you can use all of your days at once to turn in one assignment a week late. You do not need to notify us when you use a late day; we'll deduct it automatically. If you run out of late days and still submit late, your assignment will be penalized at a rate of 30% per day. If you edit your assignment after the deadline, this will count as a late submission, and we'll use the revision time as the date of submission (after a short grace period of a few minutes). We will not provide additional late time, except under exceptional circumstances, and for these we'll require documentation (e.g., a doctor's note). Please note that the late days are provided to help you deal with minor setbacks, such as routine illness or injury, paper deadlines, interviews, and computer problems; these do not generally qualify for an additional extension.

Please note that, due to the number of late days available, there will a lag between submission and grading times — we'll need to wait for the late submissions to arrive before we can finish grading.

Regrade requests: If you think there was a grading error, you'll have 9 days to submit a regrade request, using Gradescope. This will be a strict deadline, even for significant mistakes such as missing grades, so please look over your graded assignments!

Support: The counseling and psychological services center (CAPS) provides support for a variety of issues, including mental health and stress.

Grading:

Academic integrity: While you are encouraged to discuss homework assignments with other students, your programming work must be completed individually. You must also write up your solution on your own. You may not search for solutions online, or to use existing implementations of the algorithms in the assignments. Please see the Michigan engineering honor code for more information.