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.

Seminar: (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. Important! 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.

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

Papers:

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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	Slides	David Hoffmann
04.02	Anomaly detection	Questions	Philipp Laur		Silvio Galesso
11.02	Optical flow (ECCV Best Paper)	Questions	Chengxin Wang		Max Argus