Vision Transformers for Dense Prediction

[brunosan]

https://arxiv.org/pdf/2103.13413

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-theart 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.