Seminar on Current Works in Computer Vision

Prof. Thomas Brox

Computer Vision is a very active research field with many practical applications, for instance in quality control, robotics, or driver assistance systems. The goal of Computer Vision is to imitate the flexibility and robustness of the human visual system. Research has made significant progress in recent years particularly due to deep learning. Most relevant research in Computer Vision has shifted to improving deep learning methods.
In this seminar we will take a detailed look at some of the most interesting recent works. For each paper there will be one person, who performs a more detailed investigation of a research paper and its background and who will give a presentation. The presentation is followed by a discussion with all participants about the merits and limitations of the respective paper. You will learn to read and understand contemporary research papers, to give a good oral presentation, to ask questions, and to openly discuss a research problem.

Due to the Corona crisis, the seminar will be entirely held as an online seminar via Zoom, i.e., the student presentations will be given using screen sharing and the discussion will be online as well.

(2 SWS)
Thursday, 10:30-11:45,
Online via Zoom
Contact person: David Hoffmann

Beginning: Watch the following lectures before November 5:
About the seminar
Giving a good presentation
Proper scientific behavior
If you want to participate, register in HisInOne for the course, attend the Zoom meeting on November 5 11:00, and send an email with your name and your paper priorities (S1-S7, favorite paper first) to David Hoffmann before November 9.

Recommended semester:

6 (Bachelor), any (Master)
Requirements: Background in computer vision

Remarks: The language in this course is English.

There is a related Blockseminar on Biomedical Image Analysis offered by apl Prof. Olaf Ronneberger from Google DeepMind.

Topics will be assigned for both seminars via a preference voting (see above). If there are more interested students than places, places will be assigned based on priority suggestions of the HisInOne system and motivation (questions on the seminar introduction material). The date of registration is irrelevant. In particular, we want to avoid that people grab a topic and then jump off during the semester. Please have a coarse look at all available papers to make an informed decision before you commit. The listed papers are not yet sorted by the date of presentation.

The seminar will be held as an online seminar via Zoom. The link is valid for all sessions. You need a password to join. This will be sent to you by email together with additional information, such as joining by phone. The presenter should join the session 10 minutes earlier to make sure they are familiar with the equipment and can share their screen.

Please get in contact with your advisor as soon as possible, and at least 4 weeks before your presentation

Submit your presentation outline to your advisor at least 2 weeks before your presentation and meet with your advisor.

Submit your presentation slides to your advisor at least 1 week before your presentation and meet again.

All participants must read all papers and answer a few questions. The questions will be available in the 'Questions' column of the table below at least one week before the corresponding presentation. The answers must be sent to the advisor of the paper before the paper is presented. All participants must attend all online sessions.

Slides of the introductory lecture
Powerpoint template for your presentation (optional)


Date   Paper Questions  Presenting student   Slides   Advisor
17.12 Image alignment Questions Joshua Heipel Slides Philipp Schroeppel
07.01 Contextual object detection Questions Debayan Sen Slides Maria Bravo
14.01 Transfer learning Questions Rishabh Jain Yassine Marrakchi
21.01 Causal representation learning Questions Christoph Frey Slides Osama Makansi
28.01 Video representation learning Questions Mohammad Irfan SlidesDavid Hoffmann
04.02 Anomaly detection Questions Philipp Laur Silvio Galesso
11.02 Optical flow (ECCV Best Paper) Questions Chengxin Wang Max Argus