  Semantic segmentation usually suffers from a long-tail data distribution. Due
to the imbalanced number of samples across categories, the features of those
tail classes may get squeezed into a narrow area in the feature space. Towards
a balanced feature distribution, we introduce category-wise variation into the
network predictions in the training phase such that an instance is no longer
projected to a feature point, but a small region instead. Such a perturbation
is highly dependent on the category scale, which appears as assigning smaller
variation to head classes and larger variation to tail classes. In this way, we
manage to close the gap between the feature areas of different categories,
resulting in a more balanced representation. It is noteworthy that the
introduced variation is discarded at the inference stage to facilitate a
confident prediction. Although with an embarrassingly simple implementation,
our method manifests itself in strong generalizability to various datasets and
task settings. Extensive experiments suggest that our plug-in design lends
itself well to a range of state-of-the-art approaches and boosts the
performance on top of them.
