  Vision transformers have emerged as powerful tools for many computer vision
tasks. It has been shown that their features and class tokens can be used for
salient object segmentation. However, the properties of segmentation
transformers remain largely unstudied. In this work we conduct an in-depth
study of the spatial attentions of different backbone layers of semantic
segmentation transformers and uncover interesting properties.
  The spatial attentions of a patch intersecting with an object tend to
concentrate within the object, whereas the attentions of larger, more uniform
image areas rather follow a diffusive behavior. In other words, vision
transformers trained to segment a fixed set of object classes generalize to
objects well beyond this set. We exploit this by extracting heatmaps that can
be used to segment unknown objects within diverse backgrounds, such as
obstacles in traffic scenes.
  Our method is training-free and its computational overhead negligible. We use
off-the-shelf transformers trained for street-scene segmentation to process
other scene types.
