Tutorials will be held on Tuesday, August, 28th at the Campus Inffeldgasse of TU Graz.

Random Forests in Computer Vision

Tuesday, August 28, 9-13h
Inffeld Campus, HSi2, Inffeldgasse 12

Random Forests are ensembles of randomized decision trees. They can be used for classification, regression, density estimation, etc. Besides these versatile characteristics, their main benefits are simplicity, speed, robustness to noise and the ability to handle high dimensional input data. In this tutorial, we will give a detailed introduction to Random Forests, explain the critical parameters and discuss their application to many computer vision tasks, such as, object classification, detection, tracking, segmentation and pose estimation.

Christian Leistner, Microsoft Vexcel Imaging, Graz

Submodularity in Machine Learning and Computer Vision

Tuesday, August 28, 14-18h
Inffeld Campus, HSi2, Inffeldgasse 12

Many problems in machine learning and computer vision are inherently discrete, and the resulting optimization problems can become computationally very challenging. While convexity is an important property when solving continuous optimization problems, submodularity is closely tied to the tractability of many discrete problems. Even more, the characterizing property of submodular functions, diminishing marginal returns, emerges naturally in various settings and is a rich abstraction for a myriad of problems. Long recognized for its importance in combinatorial optimization and game theory, submodularity is now emerging in an increasing number of applications in machine learning and computer vision.

This tutorial introduces researchers to the concept of submodular functions, their optimization, applications and relevant results in recent research directions. Illustrative examples and animations will help develop an intuition for the concept and algorithms. The tutorial aims at providing an overview of existing results that are important to machine learners, and will provide pointers to further, detailed resources. Slides from previous tutorials can be found at submodularity.org.

The tutorial will be divided into four sections:

1. What is submodularity and what is special about it? Is my problem submodular?
2. What are example applications of submodular maximization and minimization?
3. What algorithms exist for optimizing submodular functions?
4. What are new directions?

Andreas Krause, ETH Zurich
Stefanie Jegelka, UC Berkeley