论文标题
MPLP ++：密集图形模型的快速，平行的双重级坐标上升
MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models
论文作者
论文摘要
具有成对电势的密集，离散的图形模型是一类强大的模型，这些模型用于最先进的计算机视觉和生物成像应用中。 This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle.令人惊讶的是，通过对最大产品线性编程（MPLP）算法进行较小的更改，我们得出了新的求解器MPLP ++，该求解器MPLP ++可以大大优于所有现有的求解器，包括最先进的利润率，包括最先进的树木求离者树覆盖范围的覆盖范围（trws）序列（TRWS）消息 - 备用algorith AlgorithM。此外，与TRW相比，我们的求解器高度平行，TRW与拟议的GPU和多线程CPU实现相反，这进一步提高了性能。我们还验证了算法对公开可用基准的密集问题的优势，也是6D对象姿势估算的新基准。 We also provide an ablation study with respect to graph density.
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 the low-performing solver, the Max Product Linear Programming (MPLP) algorithm, 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 (TRWS) message-passing algorithm. Additionally, our solver is highly parallel, in contrast to TRWS, 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.
