<?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%">Tourani, Siddharth</style></author><author><style face="normal" font="default" size="100%">Shekhovtsov, Alexander</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Block-Coordinate-Ascent</style></keyword><keyword><style  face="normal" font="default" size="100%">graphical models</style></keyword><keyword><style  face="normal" font="default" size="100%">Message passing algorithms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">11208 LNCS</style></volume><pages><style face="normal" font="default" size="100%">264–281</style></pages><isbn><style face="normal" font="default" size="100%">9783030012243</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle. Surprisingly, by making a small change to a low-performing solver, the Max Product Linear Programming (MPLP) algorithm [7], we derive the new solver MPLP++ that significantly outperforms all existing solvers by a large margin, including the state-of-the-art solver Tree-Reweighted Sequential (TRW-S) message-passing algorithm [17]. Additionally, our solver is highly parallel, in contrast to TRW-S, which gives a further boost in performance with the proposed GPU and multi-thread CPU implementations. We verify the superiority of our algorithm on dense problems from publicly available benchmarks as well as a new benchmark for 6D Object Pose estimation. We also provide an ablation study with respect to graph density.</style></abstract></record></records></xml>