  We introduce dense vision transformers, an architecture that leverages vision
transformers in place of convolutional networks as a backbone for dense
prediction tasks. We assemble tokens from various stages of the vision
transformer into image-like representations at various resolutions and
progressively combine them into full-resolution predictions using a
convolutional decoder. The transformer backbone processes representations at a
constant and relatively high resolution and has a global receptive field at
every stage. These properties allow the dense vision transformer to provide
finer-grained and more globally coherent predictions when compared to
fully-convolutional networks. Our experiments show that this architecture
yields substantial improvements on dense prediction tasks, especially when a
large amount of training data is available. For monocular depth estimation, we
observe an improvement of up to 28% in relative performance when compared to a
state-of-the-art fully-convolutional network. When applied to semantic
segmentation, dense vision transformers set a new state of the art on ADE20K
with 49.02% mIoU. We further show that the architecture can be fine-tuned on
smaller datasets such as NYUv2, KITTI, and Pascal Context where it also sets
the new state of the art. Our models are available at
https://github.com/intel-isl/DPT.
