Semantic parsing, edge detection, and pose estimation of human are three closely-related tasks. They present human characteristics from three complementary aspects. Compared to learning them individually, solving these tasks jointly can explore the interaction of their contextual cues. However, prior works usually study the fusion of two of them, e.g., parsing and pose, parsing and edge. In this paper, we explore how pixel-level semantics, human boundaries and joint locations can be effectively learned in a unified model. Specifically, we propose an end-to-end trainable Human Task Correlation Machine (HTCorrM) to implement the three tasks. It is asymmetric in that it supports a main task using the other two as auxiliary tasks. We also introduce a Heterogeneous Non-Local module (HNL) to discover the correlations of the three heterogeneous domains. HNL fully explores the global dependency among tasks between any two positions in the feature map. Experimental results on human parsing, pose estimation and body edge detection demonstrate that HTCorrM achieves competitive performance. We show that when designated as the main task, the accuracy of each of the three tasks is improved. Importantly, comparative studies confirm the advantages of our proposed feature correlation strategy over feature concatenation or post processing.

中文翻译：

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关于边缘、姿势和解析之间的相关性。

语义解析、边缘检测和人体姿态估计是三个密切相关的任务。它们从三个互补的方面来呈现人的特征。与单独学习它们相比，共同解决这些任务可以探索它们的上下文线索的相互作用。然而，先前的工作通常研究其中两者的融合，例如，解析和姿势，解析和边缘。在本文中，我们探讨了如何在统一模型中有效地学习像素级语义、人体边界和关节位置。具体来说，我们提出了一个端到端可训练的人工任务关联机（HTCorrM）来实现这三个任务。它是不对称的，因为它支持使用其他两个作为辅助任务的主要任务。我们还引入了一个异构非本地模块 (HNL) 来发现三个异构域的相关性。HNL 充分探索了特征图中任意两个位置之间任务之间的全局依赖关系。人体解析、姿态估计和身体边缘检测的实验结果表明，HTCorrM 具有竞争性能。我们表明，当指定为主要任务时，三个任务中每一个的准确性都会提高。重要的是，比较研究证实了我们提出的特征关联策略优于特征连接或后处理的优势。