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In addition to impressive performance, vision transformers have demonstratedremarkable abilities to encode information they were not trained to extract.For example, this information can be used to perform segmentation orsingle-view depth estimation even though the networks were only trained forimage recognition. We show that a similar phenomenon occurs when explicitlytraining transformers for semantic segmentation in a supervised manner for aset of categories: Once trained, they provide valuable information even aboutcategories absent from the training set. This information can be used tosegment objects from these never-seen-before classes in domains as varied asroad obstacles, aircraft parked at a terminal, lunar rocks, and maritimehazards.

AttEntropy: On the Generalization Ability of Supervised Semantic Segmentation Transformers to New Objects in New Domains