Probabilistic graphical models (PGMs) are now ubiquitous in a wide variety of computer vision tasks from low-level and high-level vision problems. They are expected to be of fundamental importance with regard to the task of many computer vision applications, such as denoising, stereo reconstruction, object segmentation, scene understanding, and human activity recognition. The purpose of this workshop is to bring together an examination of recent advances in PGMs with emerging problem formulations motivated by Computer Vision applications.

Submissions are invited from all areas of computer vision relevant for graphical models. Topics of interest include, but are not limited to:

• Modeling aspects in PGMs
• Inference methods for higher-order models
• MAP inference with unknown graph structure
• Inference for large scale PGMs
• Inference in hybrid continuous-discrete models
• Learning methods, including partially and weakly labeled data
• Distributed inference and learning techniques
• Anytime algorithms for inference and learning

All manuscripts will be subject to a double-blind review process. In the proceedings, accepted papers will be allocated 14 pages for the main paper excluding references.