We develop an object classification method that can learn a novel class from a single training example. In this method, experience with already learned classes is used to facilita...
Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome fo...
Anelia Angelova, Yaser S. Abu-Mostafa, Pietro Pero...
This paper describes a framework for learning probabilistic models of objects and scenes and for exploiting these models for tracking complex, deformable, or articulated objects i...
In this work we are investigating the learning benefits of e-Learning principles (a) within the context of a web-based intelligent tutor and (b) in the “wild,” that is, in real...
Bruce M. McLaren, Sung-Joo Lim, David Yaron, Kenne...
We propose an unsupervised, probabilistic method for learning visual feature hierarchies. Starting from local, low-level features computed at interest point locations, the method c...