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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. Almost all research in Computer Vision has shifted to deep learning based 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.

In the poll of the introduction meeting it was decided to have this seminar as an online seminar in Zoom.

Seminar:
(2 SWS)
Friday, 14:00-15:30 as Online Meeting
Contact person: Silvio Galesso

Recommended semester:

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

Remarks: The language in this course is English.

There is a strongly related Blockseminar on Deep Learning offered by apl Prof. Olaf Ronneberger from Google DeepMind. The introduction meeting will be jointly for both seminars.

Topics will be assigned for both seminars via a preference voting. If there are more interested students than places, first priority will be given to students who attended the introduction meeting. Afterwards, we follow the assignments of the HisInOne system. 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. If you don't attend the meeting (or not send a paper preference) but choose this seminar together with only other overbooked seminars in HisInOne, you may end up without a seminar place this semester.

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 sessions.


Material:

Giving a good presentation
Proper scientific behavior

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

Papers:

Date   Paper Questions  Presenting student   Slides   Advisor
3.6 Weakly supervised object detection questions Dimitrios Chasanidis Maria Bravo
17.6 Network visualizations Paul Kull Philipp Schroeppel
1.7 Self-supervised object discovery Sai Bharath Chandra Gutha Silvio Galesso
8.7 Dataset for embodied learning Vasudha Venkatesan Max Argus
15.7 image-text embeddings Bijay Gurung Yassine Marrakchi
22.7 Analysis of self-supervised learning Yubo Wang Simon Schrodi
29.7 Learning segmentation from image-text pairs Akshay Chandra Lagandula Jan Bechtold