We propose a probabilistic generative model for unsupervised semantic role induction, which integrates local role assignment decisions and a global role ordering decision in a uni...
We study unsupervised learning of occluding objects in images of visual scenes. The derived learning algorithm is based on a probabilistic generative model which parameterizes obj...
For scene classification, patch-level linear features do not always work as well as handcrafted features. In this paper, we present a new model to greatly improve the usefulness ...
Liwei Wang, Yin Li, Jiaya Jia, Jian Sun, David Wip...
In this paper we propose a robust object tracking algorithm using a collaborative model. As the main challenge for object tracking is to account for drastic appearance change, we ...
—A new generative model of handwriting patterns is proposed for interpreting their deformations. The model is based on feature desynchronization, which is a coupling process of
Abstract. We propose a generative model for automatic query reformulations from an initial query using the underlying subtopic structure of top ranked retrieved documents. We addre...
Debasis Ganguly, Johannes Leveling, Gareth J. F. J...
This paper presents a middle-level video representation named Video Primal Sketch (VPS), which integrates two regimes of models: i) sparse coding model using static or moving prim...
In this paper we construct an atlas that captures functional characteristics of a cognitive process from a population of individuals. The functional connectivity is encoded in a lo...
Georg Langs, Danial Lashkari, Andrew Sweet, Yanmei...
We propose a generative model based on Temporal Restricted Boltzmann Machines for transition based dependency parsing. The parse tree is built incrementally using a shiftreduce pa...
While basic principles of microtubule organization are well understood, much remains to be learned about the extent and significance of variation in that organization among cell t...