Structured Models in Computer Vision

A central question in Computer Vision is how human prior knowledge can be integrated in order to build efficient and accurate learning systems. Computer Vision and Machine Learning Researchers have come up with many different models to incorporate their prior knowledge about the domain structure - such as probabilistic graphical models, conditional random fields, or structured support vector machines - which have been applied successfully to many different areas of application. The goal of this workshop is to bring together researchers from different directions of Computer Vision and Machine Learning, and to stimulate the discussion about the shared concepts, the recent progress and the remaining problems of learning and inference algorithms for structured representations in Computer Vision.