This paper introduces LDA-G, a scalable Bayesian approach to finding latent group structures in large real-world graph data. Existing Bayesian approaches for group discovery (suc...
In the task of visual object categorization, semantic context can play the very important role of reducing ambiguity in objects' visual appearance. In this work we propose to...
Andrew Rabinovich, Andrea Vedaldi, Carolina Galleg...
We study the task to infer and to track the viewpoint onto a 3D rigid object by observing its image contours in a sequence of images. To this end, we consider the manifold of invar...
Christian Gosch, Ketut Fundana, Anders Heyden, Chr...
We propose new Continuous Hidden Markov Model (CHMM) structure that integrates feature weighting component. We assume that each feature vector could include different subsets of f...
In this paper, we present a least square kernel machine with box constraints (LSKMBC). The existing least square machines assume Gaussian hyperpriors and subsequently express the ...