The preliminary meeting will take place on Feb 4rd, 2026 from 14:00-15:00 in room 02.09.023 and under this Zoom link. The course itself will be conducted in person.

Organizers: Dominik Schnaus, Dominik Muhle, Christoph Reich Daniil Zverev

Email: afm-ss26@vision.in.tum.de

Number of participants: 12-18

Compute: We provide every team with access to 12GB Titan GPUs on our internal SLURM cluster. This should be enough since the course does not require extensive retraining of models.

Send an email with your current M.Sc. transcript to our email address by Feb. 18th (matching ends the day before!).

The subject of the email to afm-ss26@vision.in.tum.de has to be: "[AFM] Your Name"

In recent years, foundation models, i.e., models that are trained on broad datasets and can be used for different applications, have transformed computer vision and natural language processing. In this practical course, we will first get an overview of different foundation models via student presentations and then explore the applications of such models.

We envision the practical part of this course similar to a hackathon, where the goal is to build an interesting application using the given foundation model. At the end of the practical course, we will a demo session where every team will have the chance to showcase their project.

Example projects from this course are as follows.

• Diffusion Models: Adapt large pre-trained diffusion models like stable diffusion to a special use case using ControlNets.
• Depth Prediction: Build a simple VR application using general-purpose depth prediction networks and a camera tracker.
• etc.

Students will work in teams of 3 on a given topic, which will be assigned through a preference-based matching at the start of the semester. There will be three block sessions in which attendance is mandatory.

• Kick-Off Session 15.04.2026 14 p.m. (room TBD): Instructors will present the different topics and explain the course organization.
• Initial Project Session 06.05.2026: Student teams will present the theoretical ideas behind the foundation model they are working on. For this, students will have to review recent publications. Additionally, students will present their plan for their project.
• Demo Day and Final Presentation (Date: TBD): Student teams will present their practical work at the end of the semester.

Besides these mandatory meetings, teams will arrange individual supervision sessions with their respective supervisor on a regular basis (Rough guideline: 30mins meeting every two weeks). Besides that, supervisors will be available via chat.

The final grade will be determined through a weighted average of the both presentations, the project and the final report.

• Introduction to Deep Learning and/or Machine Learning and/or Computer Vision 3
• Practical experience with: Pytorch, HuggingFace, etc.