Learning with Markov Random Field Models for Computer Vision
Thomas Pock (Graz University of Technology)
Abstract: In this talk I will show how learning techniques can be used to significantly improve the quality of discrete Markov Random Field (MRF) models. I will start by discussing fast algorithms that combine dynamic programming with continuous optimization for solving MRF models. I then show how their potentials can be learned from data to achieve state-of-the-art performance for computer vision tasks such as stereo, optical flow and image segmentation.
optimization and control
Audience: researchers in the topic
Comments: The address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk
One World Optimization seminar
Series comments: Description: Online seminar on optimization and related areas
The address and password of the zoom room of the seminar are sent by e-mail on the mailinglist of the seminar one day before each talk
| Organizers: | Sorin-Mihai Grad*, Radu Ioan Boț, Shoham Sabach, Mathias Staudigl |
| *contact for this listing |