Computer Vision III: Detection, Segmentation and Tracking
Overview
The "Computer Vision III" course in winter semester 22/23 provides a comprehensive overview of methods for high-level computer vision tasks. These tasks provide the most compelling examples of computer vision and its use in the real world. Despite many challenges, we have seen substantial progress over the past years, fuelled by deep learning. Research on object detection, tracking and semantic segmentation is one the most active and impactful in computer vision.
Registration
To attend the course, please register in TUMonline.
Keep in mind that you will have to register for the exam separately later on.
Course website
All course material, announcements and the discussion forum will be provided on Moodle.
Prerequisites
- Introduction to Deep Learning (I2DL) (IN2346)
- Mathematical background: Linear algebra and calculus.
- Knowledge of Python is mandatory.
- Knowledge of PyTorch is highly recommended.
Schedule
What | When | Where | Who |
---|---|---|---|
Lecture | Tuesdays, 16:00-18:00 | 004, Hörsaal 1 "Interims II" (5416.01.004) | Nikita Araslanov |
Exercise | Thursdays, 08:00-10:00 | 00.02.001, MI HS 1, Friedrich L. Bauer Hörsaal (5602.EG.001) | Regine Hartwig Dominik Muhle |
Videos
Contact
If you have any questions that may be relevant to other students, please post in the forum on Moodle. For individual inquiries, you can send us an email to cv3-ws22@vision.in.tum.de.
Acknowledgement
The course material this semester will be heavily based on the previous iterations, which were developed and taught by Prof. Dr. Laura Leal-Taixé.