Derek Hoiem

Inferring Object Attributes Patterns

Ultimately, the goal of computer vision is to make useful inferences from imagery, and a big part of that is knowing something about the properties of nearby objects. In this talk, I'll describe our recent work on learning to identify object attributes, such as parts, materials, or shape, from images in a way that generalizes to new object categories. The tricky part is training classifiers that really predict the intended attribute, and not ones that are correlated through familiar object categories. Once we can predict attributes, we can say what is unusual about an object and more easily learn to recognize new objects. Sometimes we can even recognize new object categories from a purely verbal description (e.g., a goat has four legs, horns, and is furry).

Official inquiries about AIIS should be directed to Alexandre Klementiev (klementi AT uiuc DOT edu)
Last update: 08/30/2007