  We present our submission to the semantic segmentation contest of the Robust
Vision Challenge held at ECCV 2020. The contest requires submitting the same
model to seven benchmarks from three different domains. Our approach is based
on the SwiftNet architecture with pyramidal fusion. We address inconsistent
taxonomies with a single-level 193-dimensional softmax output. We strive to
train with large batches in order to stabilize optimization of a hard
recognition problem, and to favour smooth evolution of batchnorm statistics. We
achieve this by implementing a custom backward step through log-sum-prob loss,
and by using small crops before freezing the population statistics. Our model
ranks first on the RVC semantic segmentation challenge as well as on the
WildDash 2 leaderboard. This suggests that pyramidal fusion is competitive not
only for efficient inference with lightweight backbones, but also in
large-scale setups for multi-domain application.
