  Vision transformers have achieved leading performance on various visual tasks
yet still suffer from high computational complexity. The situation deteriorates
in dense prediction tasks like semantic segmentation, as high-resolution inputs
and outputs usually imply more tokens involved in computations. Directly
removing the less attentive tokens has been discussed for the image
classification task but can not be extended to semantic segmentation since a
dense prediction is required for every patch. To this end, this work introduces
a Dynamic Token Pruning (DToP) method based on the early exit of tokens for
semantic segmentation. Motivated by the coarse-to-fine segmentation process by
humans, we naturally split the widely adopted auxiliary-loss-based network
architecture into several stages, where each auxiliary block grades every
token's difficulty level. We can finalize the prediction of easy tokens in
advance without completing the entire forward pass. Moreover, we keep $k$
highest confidence tokens for each semantic category to uphold the
representative context information. Thus, computational complexity will change
with the difficulty of the input, akin to the way humans do segmentation.
Experiments suggest that the proposed DToP architecture reduces on average
$20\% - 35\%$ of computational cost for current semantic segmentation methods
based on plain vision transformers without accuracy degradation.
