Efficient Inference and Learning for Structured Models


May 9, 2013


Alexander G. Schwing


ETH Zurich


Sensors acquire an increasing amount of diverse information posing two challenges. Firstly, how can we efficiently deal with such a big amount of data and secondly, how can we benefit from this diversity? In this talk I will first present an approach to deal with large graphical models. The presented method distributes and parallelizes the computation and memory requirements while preserving convergence and optimality guarantees of existing algorithms. I will demonstrate the effectiveness of the approach on stereo reconstruction from high-resolution imagery. In the second part I will present a unified framework for structured prediction with latent variables which includes hidden conditional random fields and latent structured support vector machines as special cases. This framework allows to linearly combine different sources of information and I will demonstrate its efficacy on the problem of estimating the 3D room layout given a single image. For the latter problem, I will introduce a globally optimal yet efficient inference algorithm based on branch-and-bound.


Alexander G. Schwing

Alexander is a 4th year PhD student under the supervision of Prof. Marc Pollefeys, Prof. Tamir Hazan and Prof. Raquel Urtasun within the Computer Vision and Geometry (CVG) group of the computer science department of ETH Zurich (ETHZ). He got his Diploma (Master) majoring in electrical engineering with a focus on signal processing from Technical University of Munich (TUM). His research interests are in optimization algorithms (e.g., primal-dual methods), statistical models like structured predictors and parallelization of implementations for high performance computing environments.