Considering the worst-case scenario, junction tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees. Unfortunately, its main tractability assumption requires the treewidth of a corresponding MRF to be bounded strongly limiting the range of admissible applications. In fact, many practical problems in the area of structured prediction require modelling of global dependencies by either directly introducing global factors or enforcing global constraints on the prediction variables. That, however, always results in a fully-connected graph making exact inference by means of this algorithm intractable. Previous work [1]-[4] focusing on the problem of loss-augmented inference has demonstrated how efficient inference can be performed on models with specific global factors representing non-decomposable loss functions within the training regime of SSVMs. In this paper, we provide a more general framework for an efficient exact inference and extend the set of handleable problem instances by allowing much finer interactions between the energy of the core model and the sufficient statistics of the global terms. We demonstrate the usefulness of our method in several use cases, including one that cannot be handled by any of the previous approaches.

翻译：考虑到最坏的假设情况,连接树算法仍然是使用多元运行时间保证进行精确的MAP推理的最一般解决办法,不幸的是,其主要的推理性假设要求相应的管理成果框架的树枝界限严格地限制可受理申请的范围,事实上,结构化预测领域的许多实际问题要求通过直接引入全球因素或通过对预测变量施加全球限制来模拟全球依赖性。然而,这总是导致一个完全连通的图,通过这一算法难以找到确切的推理。以前的工作[1][4]侧重于损失加速推理的问题,表明在具有具体全球因素的模型上能够有效地作出推断,这些模型代表了SSVM培训制度内不可分离的损失功能。在本文件中,我们为高效精确的推理提供了更一般性的框架,并通过允许核心模型的能量与全球术语的充分统计进行更细的交互作用。我们在若干使用案例中展示了我们的方法的效用,包括以前方法无法处理的任何方法。