  Deep supervised models have an unprecedented capacity to absorb large
quantities of training data. Hence, training on multiple datasets becomes a
method of choice towards strong generalization in usual scenes and graceful
performance degradation in edge cases. Unfortunately, different datasets often
have incompatible labels. For instance, the Cityscapes road class subsumes all
driving surfaces, while Vistas defines separate classes for road markings,
manholes etc. Furthermore, many datasets have overlapping labels. For instance,
pickups are labeled as trucks in VIPER, cars in Vistas, and vans in ADE20k. We
address this challenge by considering labels as unions of universal visual
concepts. This allows seamless and principled learning on multi-domain dataset
collections without requiring any relabeling effort. Our method achieves
competitive within-dataset and cross-dataset generalization, as well as ability
to learn visual concepts which are not separately labeled in any of the
training datasets. Experiments reveal competitive or state-of-the-art
performance on two multi-domain dataset collections and on the WildDash 2
benchmark.
