Computer Vision

This course is on automatically understanding visual content such as images and videos by deep learning, with examples of domain-specific applications. The range of topics (although, not limited to): fundamentals in vision, visual representations, object detection, per-pixel labeling, video recognition, efficiency, self-supervision, etc. 

The final grade of the course consists of the following components:

• Written Exam (weighting 50%)

• Group Report: blog post about the group project (weighting 40%)

• Group Presentation: presentation about a research paper (weighting 10%)

Final grade calculation = 0.5 * Written Exam + 0.4 * Group Report + 0.1 * Group Presentation

A passing final grade for the course can only be earned when for all components at least a 5.0 is earned, and the weighted final grade is at least a 5.8.

In case of an insufficient final result, repair options may exist in accordance with Article 17A, Times and number of examinations, sub 1, of the Teaching and Examination Regulations, for:

• Written Exam: Resit opportunity

• Group Report: Repair opportunity

• Group Presentation: Repair opportunity

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 2, sub 5).