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Vision transformers have emerged as powerful tools for many computer visiontasks. It has been shown that their features and class tokens can be used forsalient object segmentation. However, the properties of segmentationtransformers remain largely unstudied. In this work we conduct an in-depthstudy of the spatial attentions of different backbone layers of semanticsegmentation transformers and uncover interesting properties.  The spatial attentions of a patch intersecting with an object tend toconcentrate within the object, whereas the attentions of larger, more uniformimage areas rather follow a diffusive behavior. In other words, visiontransformers trained to segment a fixed set of object classes generalize toobjects well beyond this set. We exploit this by extracting heatmaps that canbe used to segment unknown objects within diverse backgrounds, such asobstacles in traffic scenes.  Our method is training-free and its computational overhead negligible. We useoff-the-shelf transformers trained for street-scene segmentation to processother scene types.

AttEntropy: Segmenting Unknown Objects in Complex Scenes using the Spatial Attention Entropy of Semantic Segmentation Transformers