CAREER: Towards a Self-Taught Vision System

University Of Massachusetts Amherst, Amherst MA

Investigators

Abstract

Title: CAREER: Towards a Self-Taught Vision System PI: Erik Learned-Miller Abstract Using modern learning techniques, it is now possible to teach computers visual concepts through example-based learning. But this process is time consuming and arduous. Often large data sets must be manually collected. Machines typically do not take advantage of previously learned knowledge when performing new tasks. And when confronted with a new situation, systems fail catastrophically. The goal of this research is to make it dramatically easier to teach vision systems new skills, and to design machines that can learn tasks faster by leveraging previously learned knowledge. In short, the aim is to develop computer vision systems that are largely self-taught. More specifically, this research will focus on problems such as learning from a small number of examples; using previously learned knowledge to improve performance on novel tasks; learning properties of one object that can be used to make inferences about other objects; acquiring and organizing information autonomously; and leveraging interdisciplinary techniques to help relieve people from the burden of ``training'' computers. These capabilities are taken for granted in human beings, but represent serious shortcomings in today's computer systems. A central tenet of this work is that it is impractical to train vision systems one problem at a time, acquiring large training sets and developing training paradigms for each task to be learned. There are many scenarios in which training data are severely limited (there are limited photos of Abraham Lincoln). And ideally, computer systems should be adaptive, and not have to be prepared for each new task, especially when these new tasks are similar to previous ones. Some specific areas of investigation include learning to recognize any particular car or face from a single example, simply by watching other cars or faces as they move about; developing software for robots to continously explore the visual world and the interactions between vision and the other senses; and learning to recognize typewritten text in a font never seen before, without ANY training examples of that font. The common thread in these efforts is that they relieve the burden on the teacher of the computer. The final goal is to develop computers that can be taught simply and rapidly, and that can explore on their own. Educational initiatives will be developed in two areas. The first area is minority and low-income outreach, involving a group of students at an urban Massachusetts school. The second area involves curriculum development and curriculum guidance at the college and graduate levels at UMass, Amherst. Project web page: http://www.cs.umass.edu/~elm/CAREER