  Generating a robust representation of the environment is a crucial ability of
learning agents. Deep learning based methods have greatly improved perception
systems but still fail in challenging situations. These failures are often not
solvable on the basis of a single image. In this work, we present a
parameter-efficient temporal filtering concept which extends an existing
single-frame segmentation model to work with multiple frames. The resulting
recurrent architecture temporally filters representations on all abstraction
levels in a hierarchical manner, while decoupling temporal dependencies from
scene representation. Using a synthetic dataset, we show the ability of our
model to cope with data perturbations and highlight the importance of recurrent
and hierarchical filtering.
