Latent Layout Analysis (LLA) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. LLA defines a generative ...
Abstract. We report our work towards building user models of learner’s development based upon evidence of their interactions with an e-learning website composed of multimedia lea...
A number of recent systems for unsupervised featurebased learning of object models take advantage of cooccurrence: broadly, they search for clusters of discriminative features tha...
The algorithm presented in this paper aims to segment the foreground objects in video (e.g., people) given timevarying, textured backgrounds. Examples of time-varying backgrounds ...
We present a probabilistic generative model of visual attributes, together with an efficient learning algorithm. Attributes are visual qualities of objects, such as ‘red’, ...