  Face parsing infers a pixel-wise label to each facial component, which has
drawn much attention recently. Previous methods have shown their success in
face parsing, which however overlook the correlation among facial components.
As a matter of fact, the component-wise relationship is a critical clue in
discriminating ambiguous pixels in facial area. To address this issue, we
propose adaptive graph representation learning and reasoning over facial
components, aiming to learn representative vertices that describe each
component, exploit the component-wise relationship and thereby produce accurate
parsing results against ambiguity. In particular, we devise an adaptive and
differentiable graph abstraction method to represent the components on a graph
via pixel-to-vertex projection under the initial condition of a predicted
parsing map, where pixel features within a certain facial region are aggregated
onto a vertex. Further, we explicitly incorporate the image edge as a prior in
the model, which helps to discriminate edge and non-edge pixels during the
projection, thus leading to refined parsing results along the edges. Then, our
model learns and reasons over the relations among components by propagating
information across vertices on the graph. Finally, the refined vertex features
are projected back to pixel grids for the prediction of the final parsing map.
To train our model, we propose a discriminative loss to penalize small
distances between vertices in the feature space, which leads to distinct
vertices with strong semantics. Experimental results show the superior
performance of the proposed model on multiple face parsing datasets, along with
the validation on the human parsing task to demonstrate the generalizability of
our model.
