  The perceptual-based grouping process produces a hierarchical and
compositional image representation that helps both human and machine vision
systems recognize heterogeneous visual concepts. Examples can be found in the
classical hierarchical superpixel segmentation or image parsing works. However,
the grouping process is largely overlooked in modern CNN-based image
segmentation networks due to many challenges, including the inherent
incompatibility between the grid-shaped CNN feature map and the
irregular-shaped perceptual grouping hierarchy. Overcoming these challenges, we
propose a deep grouping model (DGM) that tightly marries the two types of
representations and defines a bottom-up and a top-down process for feature
exchanging. When evaluating the model on the recent Broden+ dataset for the
unified perceptual parsing task, it achieves state-of-the-art results while
having a small computational overhead compared to other contextual-based
segmentation models. Furthermore, the DGM has better interpretability compared
with modern CNN methods.
