Skip to content. Skip to navigation
CIM Menus

Complexity and Compositionality

Alan Yuille
Department of Statistics University of California
Los Angeles

December 10, 2014 at  1:30 PM
McConnell Engineering Rm 437

A fundamental problem of vision is how to deal with the astronomical complexity of images, scenes, and visual tasks. For example, considering the enormous input space of images and output space of objects, how can a human observer obtain a coarse interpretation of an image within less than 150 msec? And how can the observer, given more time, be able to parse the image into its components (objects, object parts, and scene structures) and reason about their relationships and actions? The same complexity problem arguably arises in most aspects of intelligence and addressing it is critical to understanding the brain and to designing artificial intelligence systems. This talk describes a research program which addresses this problem by using hierarchical compositional models which represent objects, and scene structures, in terms of elementary components which can be grouped together to form more complex structures, shared between different objects, and which are represented more abstractly in summary form. This program is illustrated by examples including: (i) low-level representations of images, (ii) segmentation and bottom-up attentional mechanisms, (iii) detection and parsing objects, (iv) estimating the 3D shapes of objects and scene structures from single images.