Block-Seminar on Deep Learning

apl. Prof. Olaf Ronneberger (Google DeepMind)

In this seminar you will learn about recent developments in deep learning with a focus on images and videos and their combination with other modalities like language. The surprising emerging capabilities of large language models (like GPT-4) open up new design spaces. Many classic computer vision tasks can be translated into the language domain and can be (partially) solved there. Understanding the current capabilities, the shortcomings and approaches in the language domain will be essential for the future Computer Vision research. So the selected papers this year focus on the key concepts used in todays large language models as well as the approaches to combine computer vision with language.

For each paper there will be one person, who performs a detailed investigation of a research paper and its background and will give a presentation (time limit is 35-40 minutes). 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. The maximum number of students that can participate in the seminar is 10.

The introduction meeting (together with Thomas Brox's seminar) will be in person, while the mid semester meeting will be online. The block seminar itself will be in person to give you the chance to practise your real-world presentation skills and to have more lively discussions

Contact person: Arian Mousakhan

Blockseminar: (2 SWS)	In person. Date: tba (2 days in the term break, Feb 2025 - Apr 2025)
	Room: tba
Beginning:	If you want to participate, attend the mandatory introduction meeting (Will be held jointly with Seminar on Current Works in Computer Vison) on October, 16th, 14:00, register in HisInOne, and submit your paper preferences before October, 21st.
Mid-Semester Lecture:	(date tba, 2 hours, via video conference) Introduction to Generative models by apl. Prof. Olaf Ronneberger (Google DeepMind)
Recommended semester:	6 (Bachelor), any (Master)
Requirements:	Background in computer vision
Remarks:	This course is offered to both Bachelor and Master students. The language of this course is English. All presentations must be given in English.
	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 intrdocution 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. 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. Students who just need to attend (failed SL from previous semester), need not send a preference for a paper, but just reply with "SL only".

Figure 1 from Movie Gen (Meta)
Videos at https://go.fb.me/MovieGenResearchVideos

Material

from Thomas Brox's seminar:

Papers:

The seminar has space for 10 students

ID	Paper Title	Comment	Student	Advisor
B1	VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models	Clever pipeline using existing databases and model to build a text-to-3D model	Upamanyu Das	Leonhard Sommer
B2	Movie Gen: A Cast of Media Foundation Models	Impressive video quality. We will select a part of this paper. See also https://ai.meta.com/research/movie-gen	Omar Swelam	Rajat Sahay
B3	UniSim: Learning Interactive Real-World Simulators	Video generation beyond image quality, good applications and showing that they can improve embodiedAI planning capabilities	Nicolas von Trott	Karim Farid
B4	The Llama 3 Herd of Models	Complete description of the internals of LLama. We will select a part of this paper.	Donat Sinani	Max Argus
B6	Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs	This is the RL approach that actually works for LLMs in practice.	Felix Ruh	Leonhard Sommer
B7	Let's Verify Step by Step	Filtering generated outputs with a process reward model (PRM) is much better than using an outcome reward model (ORM). Might be one of the ingredients in the OpenAI o1 model.	Aishwarya Dinni	Artur Jesslen
B8	Improve Mathematical Reasoning in Language Models by Automated Process Supervision	Replaces expensive human labels to train process reward models with automated labels.	Ayisha Ryhana Dawood	Simon Schrodi
B9	Training Language Models to Self-Correct via Reinforcement Learning	Self correction is an essential part of the OpenAI o1 model. The presented approach shows significant gains on Math and HumaEval	Abdul Kalam Azad	Sudhanshu Mittal
B10	Visual SKETCHPAD: Sketching as a Visual Chain of Thought for Multimodal Language Models	Cool improvement for visual reasoning: Spend more compute on "thinking" during inference.	Srikanth Shastry	Simon Ging
B11	Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution	Self-improvement and refinement in prompts for LLMs, important feature for open-endedness	Urs Micha Spiegelhalter	Jelena Bratulic