The goal of the student-led lectures is for you to develop your skills in analyze and present cutting-edge research in computer vision. Each student/team will lead one lecture on one computer vision topic.
This effort will constitute 30% of your total class grade. Your grade for this effort will be calculated as follows:
For the paper proposal, you and your teammate you should:
Identify 4-6 candidate papers that were recently published at a premiere computer vision conference (e.g., CVPR, ICCV, ECCV) on your topic. Two of these papers will be assigned as readings, with one needing to be about a specific dataset challenge (optional reading) and the other about a computer vision model (required reading).
Select a 30 minute time slot that works for all group members to meet with the instructor to discuss which papers to cover during the presentation. Available time slots will be posted via Canvas. This meeting must be at least two weeks prior to your first lecture.
Email the candidate papers to the instructor at least 48 hours in advance of the meeting.
Presentation Review
The presentation review is a chance to review the slide deck for your lecture and resolve any open questions. Expected content for each lecture is outlined in the next section. For this presentation review, you will be expected to:
Select a 30 minute time slot that works for all group members to meet with the instructor to review the lecture slides for both lectures. Available time slots will be posted via Canvas. This meeting must be at least one week prior to your first lecture.
Email the lecture slides to the instructor at least 24 hours in advance of the meeting.
Lecture
Each lecture should take about 50 minutes and consist of two parts. The first portion should: (i) define the problem, (ii) motivate the practical importance of solving this problem with a computer vision solution (i.e., applications that can/do benefit society), (iii) describe 1-2 datasets used to track progress on this problem, and (iv) describes metric(s) used to evaluate the performance of computer vision models. The second portion should introduce at least one computer vision model, covering: (i) its claimed novelty, (ii) mechanisms used to validate the claims, and (iii) open technical questions/problems. Then, the lecture will conclude with a facilitated class discussion about the lecture topic, organized by the instructor around the questions and discussion points submitted by all students. Students can incorporate materials from outside sources in their presentations (for example, content from the paper's authors or slides), but proper credit MUST be given.