Course Title: Deep Learning for Computer Vision: Seminal Work
Course ID: 263-5904-00L
Lecturers: Dr. Hermann Blum, Dr. Iro Armeni
Teaching Assistants: Rémi Pautrat, Paper Supervisors
Venue: Mo 16-18h, CAB G 57
Course Description :
This seminar covers seminal papers on the topic of deep learning for computer vision. The students will present and discuss the papers and gain an understanding of the most influential research in this area - both past and present. The objectives of this seminar are two-fold. Firstly, the aim is to provide a solid understanding of key contributions to the field of deep learning for vision (including a historical perspective as well as recent work). Secondly, the students will learn to critically read and analyse original research papers and judge their impact, as well as how to give a scientific presentation and lead a discussion on their topic.
Each student chooses one paper from the provided collection to present during the course of the seminar. The students will be supported in the preparation of their presentation by the seminar assistants.
Recommended Textbooks for Reference
- R. Gonzalez, R. Woods, Digital Image Processing (3rd Edition), 2007
- R. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision, 2004
- R. Szeliski, Computer Vision: Algorithms and Applications, 2010
- Class Material:
- Class material will be posted on Moodle. We will also use Moodle to submit assignments.
- Each student will present one paper. Everybody is encouraged to read each paper before it is being presented and engage in a discussion following the presentations. To foster interesting discussions, each paper will also be assigned two "critics" who study the paper and prepare questions for the discussion.
- Each student will be graded based on both their presentation (75%) and their participation in the assigned discussions (20%). There is a small participation grade (5%) for those that ask questions in papers even if they are not assigned to them.
- Attendance is required to pass the course (3 absences allowed).
- The class is held in person, except if otherwise stated.