Machine Learning for Computer Vision Institute of AI Faculty of Computer Science TU Dresden

Computer Vision Seminar (Summer Term 2021)



Date Time Seminar Topic
June 3rd 09:00 - 09:40 Join live ORB-SLAM: a Versatile and Accurate Monocular SLAM System
June 4th 09:40 - 10:20 Join live A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
11:00 - 11:40 (cancelled)
11:40 - 12:20 Join live An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
16:00 - 16:40 (cancelled)
June 7th 11:00 - 11:40 Join live Image to Image translation with Conditional Adversarial Networks
July 12th 11:00 - 11:40 Join live A Image-Based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era
15:20 - 16:00 Join live Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning
16:00 - 16:40 Join live Fusion moves for correlation clustering
July 13th 09:40 - 10:20 Join live K-convexity Shape Priors for Segmentation
10:20 - 11:00 Join live Regions with CNN features - R-CNNs
July 14th 09:00 - 09:40 Join live Guided Image Generation with Conditional Invertible Neural Networks
09:40 - 10:20 Join live LocalViT: Bringing Locality to Vision Transformers
10:20 - 11:00 Join live Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
11:00 - 11:40 Join live A Style-Based Generator Architecture for Generative Adversarial Networks
12:20 - 13:00 Join live Joint Variational Method of Shape of Shading and Stereo
15:20 - 16:00 Join live Training data-efficient image transformers & distillation through attention
16:00 - 16:40 Join live Transformers in Vision: A Survey
16:40 - 17:20 Join live Text-based Editing of Talking-head Video
17:20 - 18:00 Join live Deep Residual Learning for Image Recognition


In this seminar, participating students will read, understand, prepare and present the contents of a research article or book chapter on a topic from the field of computer vision. The article or book chapter will be chosen by the student from the list below, or suggested by the student for approval in the beginning of the term. The preparation will include relevant foundational and related work. By attending at least 10 presentations of their peers, students will get an overview of diverse topics in the field of computer vision.


Prerequisites for taking this course are a strong background in mathematics (esp. linear algebra and analysis) and theoretical computer science. For some of the suggested articles and book chapters, additional knowledge from the field of mathematical optimization or machine learning is required.


Requirements for passing this course are:


In their preparation, participating students are supervised remotely, by email. They are strongly encouraged to report on their progress briefly, every Friday, by email.

Suggested Research Articles

Image Decomposition



Pose Estimation

Motion Analysis


Deep Learning for Computer Vision

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