  Understanding the context of complex and cluttered scenes is a challenging
problem for semantic segmentation. However, it is difficult to model the
context without prior and additional supervision because the scene's factors,
such as the scale, shape, and appearance of objects, vary considerably in these
scenes. To solve this, we propose to learn the structures of objects and the
hierarchy among objects because context is based on these intrinsic properties.
In this study, we design novel hierarchical, contextual, and multiscale
pyramidal representations to capture the properties from an input image. Our
key idea is the recursive segmentation in different hierarchical regions based
on a predefined number of regions and the aggregation of the context in these
regions. The aggregated contexts are used to predict the contextual
relationship between the regions and partition the regions in the following
hierarchical level. Finally, by constructing the pyramid representations from
the recursively aggregated context, multiscale and hierarchical properties are
attained. In the experiments, we confirmed that our proposed method achieves
state-of-the-art performance in PASCAL Context.
