Catalog Description: Students will learn about the newest development and understanding of algorithms, systems, and best practices in computer vision and deep learning research and engineering. The class will focus on new and hot topics including: the family of vision and language transformers, diffusion models, neural scene representation and rendering, massive scale supervised and unsupervised learning, and neural network foundations. Students will read research papers and code implementations as exercises, present and lead discussions, and work on projects that are closely related to those interesting topics. The course is designed to provide students a state-of-the-art perspective to current computer vision and deep learning research with the goal to inspire future impactful research on those hot topics.

Prerequisties: Students should have knowledge about computer vision and deep learning basics.

Class Location and Times:

Tue & Thu	9:35 am - 10:50 am	222 Boyd Miller Plant Science Building, Room 2102
Wed	10:20 am - 11:10 am	222 Boyd Miller Plant Science Building, Room 1102
Google Map Direction

Reading Materials:

• “Deep Learning” (2016) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. A free online version is available here.
• “Dive into Deep Learning” (2021) by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. A free online version is available here.
• “Computer Vision: Algorithms and Applications” (2022, 2nd ed) by Richard Szeliski. A free online version is available here.

Student Outcomes:

1. Demonstrate understanding of computer vision and deep neural network fundamentals.
2. Gain experience deploying deep learning models on computer vision and nlp problems.
3. Enhanced research skills, including reading papers, performing literature research, and analyzing cutting-edge research.

Instructor Contact:

Prof. Jin Sun
Office Hours: Thursdays 11 - 12 am or By Appointment
Office: 804 Boyd
Email: jinsun@uga.edu

Evaluation and Grading: The final course grade will be weighted as the follows:

Paper presentations	40%
Paper readings	20%
Course project	40%

Paper presentations: For each required reading paper, we will have around two students presenting the paper (about 45 mins) and leading the class discussion (about 20 mins). Since we will cover a wide-range of topics and problems, a good coverage of background or context for each paper will be very useful. Several essential components of a high-quality presentation are:

1. Background: What problem this paper is working on? Why is it important?
2. Related work: Before this work, how do other people work on this problem?
3. Motivation of the proposed work: What makes the author(s) propose this work?
4. Method: Describe the proposed algorithm and/or workflow.
5. Evaluation: How is the method evaluated? What are the results?
6. Summary and future directions: What is the main takeaway message? What follow-up work can be done?

Each student might present 1-2 times over the whole semester.

Paper readings: Every week, you will pick one of the required papers and write a short reading summary. This is to practice your skills in reading literature and critical thinking. Such a summary should include:

1. Main message: What does the paper propose? Describe the main points in two or three sentences.
2. Pros: What are the strengths of this paper? 1-3 bullet points are fine.
3. Cons: What are the weaknesses of this paper? 1-3 bullet points are fine.
4. Future directions: Discuss possible follow-up work from this paper. Two or three sentences are fine.

You don't need to write a summary for the week you are presenting a paper.

Team Project: You will work in a team on a course project. Each team should have 2-3 members. You are encouraged to design the project to solve a real-world application using deep learning and computer vision. Feel free to use any programming language or software packages of your choice. The schedule for the project is as follows:

1. Project Proposal: The project proposal should clearly state what your team plan to do. It should be four pages long (not including references). It should contain a timeline. You should list the questions the project will address and that will be discussed in the report. You should list what software you will be using or will build upon. Describe the datasets you will use and how will you know if the project is successful. Describe the hypotheses you will test and the related work. You should be able to reuse much of the text for the final report.


2. Project Milestone: You can re-use the project proposal for this report but expand it with additional content. You should talk about preliminary results and/or other measurable items listed in the proposal.


3. Project Report and Presentation: The final report contains a complete description of the project: what you have done and what the result looks like. It should be about six to eight pages long (not including references). You are encouraged to format it in CVPR format. We will have a presentation session for all projects at the last day of the class. Make sure every member in your team participate in the presentation.

Class Schedule

School of Computing | University of Georgia | 2023