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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

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-- <html><font color="red"> The post-exam review will take place on Wednesday, August 23th from 16:00 - 18:00 in the room 02.09.023.  </font></html>  \\+- <html><font color="red"> The post-exam review (Klausureinsicht) will take place on Wednesday, August 23th from 16:00 - 18:00 in the room 02.09.023.  </font></html>  \\
  
 Many important machine learning, computer vision and image processing problems can be cast as convex energy minimization problems, e.g. training of SVMs, logistic regression, low-rank and sparse matrix decomposition, denoising, segmentation, or multiframe blind deconvolution. In this lecture we will discuss first order convex optimization methods to implement and solve the aforementioned problems efficiently. Particular attention will be paid to problems including constraints and non-differentiable terms, giving rise to methods that exploit the concept of duality such as the primal-dual hybrid gradient method or the alternating directions methods of multipliers. This lecture will cover the mathematical background needed to understand why the investigated methods converge as well as the efficient practical implementation.  Many important machine learning, computer vision and image processing problems can be cast as convex energy minimization problems, e.g. training of SVMs, logistic regression, low-rank and sparse matrix decomposition, denoising, segmentation, or multiframe blind deconvolution. In this lecture we will discuss first order convex optimization methods to implement and solve the aforementioned problems efficiently. Particular attention will be paid to problems including constraints and non-differentiable terms, giving rise to methods that exploit the concept of duality such as the primal-dual hybrid gradient method or the alternating directions methods of multipliers. This lecture will cover the mathematical background needed to understand why the investigated methods converge as well as the efficient practical implementation. 

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

15.10.2022

WACV 2023

We have two papers accepted at WACV 2023. Check out our publication page for more details.

More