Abstract. We propose a unifying framework for polyhedral approximation in convex optimization. It subsumes classical methods, such as cutting plane and simplicial decomposition, bu...
Although steady progress has been made in recent stereo algorithms, producing accurate results in the neighborhood of depth discontinuities remains a challenge. Moreover, among th...
Learning general functional dependencies is one of the main goals in machine learning. Recent progress in kernel-based methods has focused on designing flexible and powerful input...
Ioannis Tsochantaridis, Thomas Hofmann, Thorsten J...
While there has been substantial progress in segmenting natural im-
ages, state-of-the-art methods that perform well in such tasks unfortunately tend
to underperform ...
A. Lucchi, K. Smith, R. Achanta, V. Lepetit, P. Fu...
Treating classification as seeking minimum cuts in the appropriate graph has proven effective in a number of applications. The power of this approach lies in its ability to incorp...