We propose a novel and robust model to represent and learn generic 3D object categories. We aim to solve the problem of true 3D object categorization for handling arbitrary rotati...
In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter ...
We studied the perceived effects of pair programming (PP) compared to solo programming in a large scale, industrial software development context. We surveyed developers (N=28) reg...
Abstract. We address the problem of articulated human pose estimation by learning a coarse-to-fine cascade of pictorial structure models. While the fine-level state-space of pose...
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...