Deep Segmentation Networks

Nicolas Le Roux, Nicolas Heess, Jamie Shotton, John Winn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There are two dominating trends when it comes to building a generative model of images: one may have a fully generic model, often so general that it struggles to learn complex structures, or a model relying heavily on prior knowledge, often too restrictive to learn about the wide variety of images. This work aims at combining the advantages of general low-level generative models and powerful layer-based and hierarchical models, with the hope of being a first step towards richer, more flexible models of images. It incorporates features from both groups of works mentioned above: (i) the modeling of an image as the combination of multiple objects occluding each other, each object having its own appearance and shape; (ii) the use of fully generic models for these appearances and shapes, based on Restricted Boltzmann Machines (RBMs); (iii) a hierarchical structure to model objects of all sizes and at all scales.
Original languageEnglish
Title of host publicationThe Learning Workshop Snowbird
Publication statusPublished - 2010

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