CS1674/2074: Introduction to Computer Vision, Fall 2023

Location (class and office hours): Sennott Square 5313
Class time: Monday and Wednesday, 1pm-2:15pm
Instructor: Adriana Kovashka (email: kovashka AT cs DOT pitt DOT edu; use "CS1674" at the beginning of the subject line)
Instructor's office hours: Monday and Wednesday, 11am-1pm
Instructor's office: Sennott Square 5325
TA: Mesut Erhan Unal (email: meu6 AT pitt DOT edu)
TA's office hours: Monday 4pm-5pm (Zoom), Wednesday 4pm-5pm (Zoom), Friday 4pm-5pm (in person)
TA's office: Sennott Square 5404; Zoom link
Canvas: used for announcements and assignment submission

Overview

 Course description: In this class, students will learn the basics of modern computer vision. The first major part of the course will cover fundamental concepts such as image filtering, edge detection, feature extraction, texture description, and grouping and fitting. The second part will focus on visual recognition. We will study state of the art approaches to object recognition and detection, examine the interplays between vision and language, learn how to model motion and actions, and briefly overview generative techniques. We will cover recently popular techniques such as convolutional, recurrent neural networks, and transformers. We will also discuss a few topics from recent computer vision conferences. The course format includes lectures, in-class activities, exams, and weekly homework assignments.

Prerequisites: CS1501

Programming languages: We will use Matlab, which is available for download for free using Software Downloads in My Pitt. Please use the latest version.

Textbooks: We will have required readings from the following textbooks: You can also refer to the following textbooks for additional explanations: [top]

Policies

Grading

Grading for CS 1674 will be based on the following components: Grading for CS 2074 will be based on the following components:

Homework Submission Mechanics

Homework is due at 11:59pm on the due date. You will submit your homework using Canvas. Navigate to the Canvas page for CS1674/2074, and the corresponding homework ID. You should submit a single zip file with .m files (and images/results if requested). Name the file YourFirstName_YourLastName.zip. Please comment your code! Grades will be posted on Canvas as well.

Exams

There will be two in-class exams. The second exam is not cumulative and will not cover material from the first exam. There will be no make-up exams unless you or a very close friend/relative is seriously ill!

Project (CS 2074 only)

Students will complete a project which studies in more depth one of the topics we cover in class. Students should work in groups of two or three. These projects should focus on one of the following: In the project proposal (due Oct. 6), students should include the following: clear problem statement, extensive literature review, detailed outline of the approach, and planned experimental setup. Students are encouraged to discuss a draft of the proposal with the instructor before the proposal is due. Proposals should be 2-3 pages long.

The mid-semester project status report (due Nov. 3) will describe the students' progress on the project, and any problems encountered along the way. The status report should use a known conference format (e.g. CVPR/ICCV/ECCV), but can be more informal than a conference paper. The progress report should include the following sections: Introduction, Related Work, Approach, and Results. In Results, include your experimental setup (this can change later). If you have results but they do not yet look great, include them anyway. Comment on any challenges encountered as well.

The project presentation, scheduled for the last day of class (Dec. 6), will describe the students' approach and their experimental findings in a clear and engaging fashion. This will be a chance to get feedback from the class before final submission of your report. Presentations will be about 10-15 minutes long. Please submit a copy of your slides to Canvas on the same day as your presentation.

The project final report (due Dec. 8) should be formatted and should read like a conference paper, with clear problem definition and argumentation of why this problem is important, overview of related work, detailed explanation of the approach, and well-motivated experimental evaluation. Đ•ach student should document what part of the project they did, and how duties and tasks were divided.

Participation

Students are expected to regularly attend the class lectures, and should actively engage in in-class discussions. Attendance will not be taken, but keep in mind that if you don't attend, you cannot participate. You can actively participate by, for example, responding to the instructor's or others' questions, asking questions or making meaningful remarks and comments about the lecture, or bringing in relevant articles you saw in the news. The grading rubric will be as follows: 1 = you attended infrequently, 2 = you attended frequently but did not speak in class, 3 = you attended frequently and spoke a few times, 4 = you participated frequently, 5 = you participated every other week or more. Near the end of the course, you should submit a text response on Canvas explaining how you participated during the semester, to complement the instructor's observations.

Homework Late Policy

You get 3 "free" late days counted in minutes, i.e., you can submit a total of 72 hours late. For example, you can submit one homework 12 hours late, and another 60 hours late. The 72-hour "budget" is total for all assignments, NOT per assignment. Once you've used up your free late days, you will incur a penalty of 25% from the total assignment credit possible for each late day. A late day is anything from 1 minute to 24 hours. The free late days only apply to homework assignments (HW1-HW9). Participation and project submissions cannot be submitted late.

Collaboration Policy and Academic Honesty

You will do your work (exams and homework) individually and without the help of artificial intelligence systems (e.g. ChatGPT). The work you turn in must be your own work. You are allowed to discuss the assignments with your classmates, but do not look at code they might have written for the assignments, or at their written answers. You are also not allowed to search for or generate code on the internet, use solutions posted online unless you are explicitly allowed to look at those, or to use Matlab's implementation if you are asked to write your own code. When in doubt about what you can or cannot use, ask the instructor! A first offense will cause you to get 0% credit on the assignment. A report will be filed with the school. A second offense will cause you to fail the class and receive disciplinary penalty. Please consult SCI's Academic Integrity Country and Pitt's Academic Integrity Guidelines.

Note on Disabilities

If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services (DRS), 140 William Pitt Union, (412) 648-7890, drsrecep@pitt.edu, (412) 228-5347 for P3 ASL users, as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.

Note on Medical Conditions

If you have a medical condition which will prevent you from doing a certain assignment, you must inform the instructor of this before the deadline. You must then submit documentation of your condition within a week of the assignment deadline.

Statement on Classroom Recording

To ensure the free and open discussion of ideas, students may not record classroom lectures, discussion and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student's own private use.

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Schedule
Date Chapter Topic Readings Lecture slides Due
8/28 Intro Introduction (incl. review) Szeliski Sec. 1.1, 1.2; Grauman/Leibe Ch. 1 pptx pdf

tutorial.m myfunction.m myotherfunction.m pittsburgh.png
8/30
9/6
9/11 Low-level vision Filtering and texture Szeliski Sec. 3.2 pptx pdf

filtering.m butterfly.jpg
9/13 HW1
9/18
9/20 Feature detection and description Szeliski Sec. 7.1, Grauman/Leibe Ch. 2, 3, Sec. 4.2;
SIFT paper by David Lowe		 pptx pdf

autocorr_surface.m blobs.m butterfly3.png 		HW2
9/25
9/27 HW3
10/2 Grouping: Edges, lines, circles and segments Szeliski Sec. 5.2.1, 5.2.2, 7.2, 7.5, 7.4.2;
Grauman/Leibe Ch. 5.2		 pptx pdf
10/4 HW4;
proposal (10/6)
10/9 Multiple views Szeliski Sec 2.1, 3.6.1, 11.3, 12.1.1; Grauman/Leibe Sec. 5.1 pptx pdf
10/11 HW5
10/16 First exam
10/18 High-level vision Intro to recognition Szeliski Sec. 5.1, 6.1, 6.2; Grauman/Leibe Ch. 6, 7, Sec. 8.1, 10.1, 10.2, 11.3; Bishop PRML Sec. 1.1,
Bishop PRML Sec. 7.1		 pptx pdf
10/23 HW6
10/25
10/30 Convolutional neural networks Szeliski Sec. 5.3, 5.4, 5.5.3; Karpathy's notes, Module 1, Module 2 pptx pdf
11/1 HW7;
status (11/3)
11/6
11/8 Object recognition, detection, segmentation Szeliski Sec. 6.3, 6.4; Grauman/Leibe Sec. 8.2, 9, 10.3, 11.1, 11.2, 11.5;
Girshick CVPR 2014, Ren NIPS 2015, Redmon CVPR 2016, Radford ICML 2021 		pptx pdf
11/13
11/15 Sequences: Vision and language, video and motion Szeliski Sec. 5.5.1, 5.5.2, 6.5, 6.6; blog1, blog2,
Karpathy CVPR 2015, Vaswani NeurIPS 2017 		pptx pdf HW8
11/27
11/29
12/4 Unsupervised learning Szeliski Sec. 5.5.4; Doersch ICCV 2015, Chen ICML 2020, Goodfellow NIPS 2014, Zhu ICCV 2017, Rombach CVPR 2022 pptx pdf HW9
12/6 Project presentations
(speakers: CS2074 students)		 presentation;
report (12/8)
12/13 Second exam (12-1:15pm, 5129 Sennott Square) participation notes

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Resources

 This course was inspired by the following courses: Tutorials: Some computer vision datasets: Some code and frameworks of interest: [top]