  Single encoder-decoder methodologies for semantic segmentation are reaching
their peak in terms of segmentation quality and efficiency per number of
layers. To address these limitations, we propose a new architecture based on a
decoder which uses a set of shallow networks for capturing more information
content. The new decoder has a new topology of skip connections, namely
backward and stacked residual connections. In order to further improve the
architecture we introduce a weight function which aims to re-balance classes to
increase the attention of the networks to under-represented objects. We carried
out an extensive set of experiments that yielded state-of-the-art results for
the CamVid, Gatech and Freiburg Forest datasets. Moreover, to further prove the
effectiveness of our decoder, we conducted a set of experiments studying the
impact of our decoder to state-of-the-art segmentation techniques.
Additionally, we present a set of experiments augmenting semantic segmentation
with optical flow information, showing that motion clues can boost pure image
based semantic segmentation approaches.
