Current Topics / Important Developments in Computer Vision and Machine Learning
Semester: |
SS 2020 |
Type: |
Seminar |
Lecturer: |
|
Credits: |
4 ECTS credits |
Find a list of current courses on the Teaching page.
Due to the ongoing corona situation all lectures and exercises will be held online. Students should register for the lectures on the RWTH Online system to get notifications via Moodle.
Seminar Description
Computer Vision is a very active research field with many interesting applications. Many of its recent successes are due to advances in Machine Learning research. It is therefore useful to study the two fields together and to draw cross-links between them.
The conferences with the strongest impact in Computer Vision are CVPR, ICCV, and ECCV, whereas NIPS and ICML have the strongest impact on the Machine Learning community. In this seminar we will discuss recent results presented at those conferences with a focus on the most interesting and innovative ideas. Participating students have the chance to get familiar with state-of-the-art solutions to problems in Computer Vision and Machine Learning and will get an insight into the involved techniques.
Successful participants will be awarded 4 ECTS credits.
Prerequisites
Successful completion of at least one the lectures Computer Vision, Machine Learning, Computer Vision 2, Advanced Machine Learning, Pattern Recognition and Neural Networks, or evidence of equivalent knowledge.
Material
- Ethical Guidelines for the Authoring of Academic Work in German and English
- Declaration of Compliance in German and English
- LaTeX for the report (mandatory!): Template
- Slide template (optional): PPT, Keynote, LaTeX
Schedule
- Introductory: April 22
- Outline due: May 18
- Report due: June 15
- Slides due: June 29
- Presentations: 3 days in the week of July 20-24 - Please hand in your corrected report at this date!
For all questions concerning the seminar, please contact:
Ali Athar (athar@vision.rwth-aachen.de)
UMIC Research Centre
Mies-van-der-Rohe-Strasse 15, Room 129