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Advanced Deep Learning for Computer Vision: Visual Computing (IN2390)

Course 0000000538 in WS 2021/2

General Data

Course Type lecture
Semester Weekly Hours 2 SWS
Organisational Unit Informatics 28 - Associate Professorship of Visual Computing (Prof. Nießner)
Lecturers Matthias Nießner
Dates Mon, 10:00–12:00, virtuell

Assignment to Modules

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks The lectures will provide extensive theoretical aspects of neural networks and in particular deep learning architectures, specifically for advanced methods in the field of Computer Vision. The practical sessions will be key, students shall get familiar with Deep Learning through hours of training and testing. They will work with PyTorch and implement advanced network architectures. The project will have a focus on visual computing, including the following topics: - neural rendering - generative neural networks (GANs) - neural radiance fields - deep fake generation - media forensics (forgery detection) - scene reconstruction (multi-view, depth sensors, etc.) - generative geometric models - semantic scene understanding (object detection, instance segmentation, semantic segmentation) - 3D scene understanding for autonomous driving (e.g., with Lidar/Radar) - reinforcement learning (e.g., for 3d modeling, 3d auto-scanning, 3d navigation) - natural language processing (NLP) for scene understanding We recommend to take a look at the recent list of publications at https://niessnerlab.org/ to get a better idea of recent research projects.
Links TUMonline entry
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