<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Schlesinger, Dmitrij</style></author><author><style face="normal" font="default" size="100%">Vetrov, Dmitry</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferring M-best diverse labelings in a single one</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">2015 Inter</style></volume><pages><style face="normal" font="default" size="100%">1814–1822</style></pages><isbn><style face="normal" font="default" size="100%">9781467383912</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the task of finding M-best diverse solutions in a graphical model. In a previous work by Batra et al. an algorithmic approach for finding such solutions was proposed, and its usefulness was shown in numerous applications. Contrary to previous work we propose a novel formulation of the problem in form of a single energy minimization problem in a specially constructed graphical model. We show that the method of Batra et al. can be considered as a greedy approximate algorithm for our model, whereas we introduce an efficient specialized optimization technique for it, based on alpha-expansion. We evaluate our method on two application scenarios, interactive and semantic image segmentation, with binary and multiple labels. In both cases we achieve considerably better error rates than state-of-the art diversity methods. Furthermore, we empirically discover that in the binary label case we were able to reach global optimality for all test instances.</style></abstract></record></records></xml>