Lecture: Deep Learning for Computer Vision II: Advanced Topics

Main coordinator of the lecture since 2022, shared conception and lecturing since 2021. I contributed to the installments: WS2021, WS2022, WS2023, WS2024, WS2025

This lecture series explores advanced topics in computer vision and machine learning, starting with an introduction and progressing through weakly and semi-supervised learning techniques. It then covers visual transformers, from foundational concepts to specific tasks, and delves into representation learning. The course bridges vision and language, examines efficient architectures and fine-tuning strategies, and introduces generative models. In the later stages, it addresses uncertainty, interpretability, continual learning, and visual in-context learning. It concludes with interactive segmentation techniques, including the Segment Anything Model (Website).