带注意力的图解模型对特定情境的独立性和对知觉分组的应用

离散无定向图形模型，也被称为马尔科夫随机场（MRF），可以灵活地编码多个变量的概率相互作用，并在广泛的问题上得到成功的应用。然而，离散MRFs的一个众所周知但却鲜有研究的局限性是，它们不能捕捉特定环境的独立性（CSI）。现有的方法需要精心开发的理论和专门建立的推理方法，这限制了它们的应用，使其只能用于小规模的问题。在本文中，我们提出了马尔科夫注意力模型（MAM），一个包含注意力机制的离散MRF系列。注意机制允许变量动态地关注其他一些变量，而忽略其他变量，并能在MRF中捕获CSI。一个MAM被表述为一个MRF，使它能够从现有的丰富的MRF推理方法中获益，并扩展到大型模型和数据集。为了证明MAM在规模上捕捉CSI的能力，我们应用MAM来捕捉一种重要的CSI类型，这种CSI存在于感知分组的循环计算的符号方法中。在最近提出的两个合成感知分组任务和现实图像上的实验表明，与强循环神经网络基线相比，MAM在样本效率、可解释性和通用性方面具有优势，并验证了MAM在规模上有效捕获CSI的能力。

原文题目：Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping

原文：Discrete undirected graphical models, also known as Markov Random Fields (MRFs), can flexibly encode probabilistic interactions of multiple variables, and have enjoyed successful applications to a wide range of problems. However, a well-known yet little studied limitation of discrete MRFs is that they cannot capture context-specific independence (CSI). Existing methods require carefully developed theories and purpose-built inference methods, which limit their applications to only small-scale problems. In this paper, we propose the Markov Attention Model (MAM), a family of discrete MRFs that incorporates an attention mechanism. The attention mechanism allows variables to dynamically attend to some other variables while ignoring the rest, and enables capturing of CSIs in MRFs. A MAM is formulated as an MRF, allowing it to benefit from the rich set of existing MRF inference methods and scale to large models and datasets. To demonstrate MAM's capabilities to capture CSIs at scale, we apply MAMs to capture an important type of CSI that is present in a symbolic approach to recurrent computations in perceptual grouping. Experiments on two recently proposed synthetic perceptual grouping tasks and on realistic images demonstrate the advantages of MAMs in sample-efficiency, interpretability and generalizability when compared with strong recurrent neural network baselines, and validate MAM's capabilities to efficiently capture CSIs at scale.