We study vision science ‒ the computational principles underlying computer vision, robot vision, and human vision. We are interested in building computer systems that automatically understand visual scenes, both inferring the semantics and extracting 3D structure for a large variety of environments. Our research is also closely related to computer graphics, perception and cognition, cognitive neuroscience, machine learning, HCI, NLP and AI in general.
At the moment, we focus on leveraging Big 3D Data for Visual Scene Understanding (e.g. RGB-D sensors, CAD models, depth, multiple viewpoints, panoramic fields of view), to look for the right representations of visual scenes that realistically describe the world. We believe that it is critical to consider the role of a computer as an active explorer in a 3D world, and learn from rich 3D data that is close to the natural input that humans have.
WordNet is a resource used by researchers attempting to get computers to understand English (and any other language for which a WordNet exists).
Viewing a human language as a very large graph provides a theoretical framework for creating algorithms for understanding the meaning of words
in a text (e.g. determining if "fly" is an insect or a ball hit into left field) and translating documents between languages. We are working on both making WordNet more effective for these tasks and creating new approaches that use WordNet.