论文标题
基于骨架的动作识别的变分条件依赖隐藏的马尔可夫模型
Variational Conditional Dependence Hidden Markov Models for Skeleton-Based Action Recognition
论文作者
论文摘要
隐藏的马尔可夫模型（HMMS）包括一种强大的生成方法，用于建模顺序数据和时间序列。但是，通常采用的假设当前时间框架对单个或多个直接前帧的依赖性是不现实的。现实世界中可能存在更复杂的动态。本文重新审视了常规的顺序建模方法，旨在解决捕获时间变化的时间依赖模式的问题。为此，我们提出了HMM的不同公式，从而从数据中动态地推断了对过去帧的依赖性。具体而言，我们通过假设附加的潜在变量层来引入分层扩展。在其中，（时间变化的）时间依赖模式被视为进行推理的潜在变量。我们利用来自变分贝叶斯框架的实体参数，并根据前向算法得出可拖动的推理算法。正如我们在实验上显示的那样，我们的方法可以对高度复杂的顺序数据进行建模，并可以有效地处理缺少值的数据。
Hidden Markov Models (HMMs) comprise a powerful generative approach for modeling sequential data and time-series in general. However, the commonly employed assumption of the dependence of the current time frame to a single or multiple immediately preceding frames is unrealistic; more complicated dynamics potentially exist in real world scenarios. This paper revisits conventional sequential modeling approaches, aiming to address the problem of capturing time-varying temporal dependency patterns. To this end, we propose a different formulation of HMMs, whereby the dependence on past frames is dynamically inferred from the data. Specifically, we introduce a hierarchical extension by postulating an additional latent variable layer; therein, the (time-varying) temporal dependence patterns are treated as latent variables over which inference is performed. We leverage solid arguments from the Variational Bayes framework and derive a tractable inference algorithm based on the forward-backward algorithm. As we experimentally show, our approach can model highly complex sequential data and can effectively handle data with missing values.
