Traditionally, machine learning approaches for information extraction require human annotated data that can be costly and time-consuming to produce. However, in many cases, there ...
Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. Intuitively, discovering a good feature rep...
Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qi...
In this paper, we address the problem of motion recognition using event-based local motion representations. We assume that similar patterns of motion contain similar events with c...
We pose the problem of 3D human tracking as one of inference in a graphical model. Unlike traditional kinematic tree representations, our model of the body is a collection of loos...
Leonid Sigal, Sidharth Bhatia, Stefan Roth, Michae...
Graphical models have become the basic framework for topic based probabilistic modeling. Especially models with latent variables have proved to be effective in capturing hidden str...