  Scene Parsing is a crucial step to enable autonomous systems to understand
and interact with their surroundings. Supervised deep learning methods have
made great progress in solving scene parsing problems, however, come at the
cost of laborious manual pixel-level annotation. To alleviate this effort
synthetic data as well as weak supervision have both been investigated.
Nonetheless, synthetically generated data still suffers from severe domain
shift while weak labels are often imprecise. Moreover, most existing works for
weakly supervised scene parsing are limited to salient foreground objects. The
aim of this work is hence twofold: Exploit synthetic data where feasible and
integrate weak supervision where necessary. More concretely, we address this
goal by utilizing depth as transfer domain because its synthetic-to-real
discrepancy is much lower than for color. At the same time, we perform weak
localization from easily obtainable image level labels and integrate both using
a novel contour-based scheme. Our approach is implemented as a teacher-student
learning framework to solve the transfer learning problem by generating a
pseudo ground truth. Using only depth-based adaptation, this approach already
outperforms previous transfer learning approaches on the popular indoor scene
parsing SUN RGB-D dataset. Our proposed two-stage integration more than halves
the gap towards fully supervised methods when compared to previous
state-of-the-art in transfer learning.
