2 citations · 2 across the 3 of their papers we have counts for
4 papers
Generative learning for deep networks
Boris Flach, Alexander Shekhovtsov, Ondrej Fikar
Learning, taking into account full distribution of the data, referred to as generative, is not feasible with deep neural networks (DNNs) because they model only the conditional dis…
Scalable Full Flow with Learned Binary Descriptors
Gottfried Munda, Alexander Shekhovtsov, Patrick Knöbelreiter +1
We propose a method for large displacement optical flow in which local matching costs are learned by a convolutional neural network (CNN) and a smoothness prior is imposed by a con…
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
Alexander Kirillov, Alexander Shekhovtsov, Carsten Rother +1
We consider the problem of jointly inferring the M-best diverse labelings for a binary (high-order) submodular energy of a graphical model. Recently, it was shown that this problem…
Higher Order Maximum Persistency and Comparison Theorems
Alexander Shekhovtsov
We address combinatorial problems that can be formulated as minimization of a partially separable function of discrete variables (energy minimization in graphical models, weighted…