We study an extension of the "standard" learning models to settings where observing the value of an attribute has an associated cost (which might be different for differ...
In this paper we study interest point descriptors for image matching and 3D reconstruction. We examine the building blocks of descriptor algorithms and evaluate numerous combinati...
Many practical applications require that distance measures to be asymmetric and context-sensitive. We introduce Context-sensitive Learnable Asymmetric Dissimilarity (CLAD) measure...
We present a novel framework to reliably learn scene entry and exit locations using coherent motion regions formed by weak tracking data. We construct “entities” from weak trac...
The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...