  This paper introduces Content-aware Token Sharing (CTS), a token reduction
approach that improves the computational efficiency of semantic segmentation
networks that use Vision Transformers (ViTs). Existing works have proposed
token reduction approaches to improve the efficiency of ViT-based image
classification networks, but these methods are not directly applicable to
semantic segmentation, which we address in this work. We observe that, for
semantic segmentation, multiple image patches can share a token if they contain
the same semantic class, as they contain redundant information. Our approach
leverages this by employing an efficient, class-agnostic policy network that
predicts if image patches contain the same semantic class, and lets them share
a token if they do. With experiments, we explore the critical design choices of
CTS and show its effectiveness on the ADE20K, Pascal Context and Cityscapes
datasets, various ViT backbones, and different segmentation decoders. With
Content-aware Token Sharing, we are able to reduce the number of processed
tokens by up to 44%, without diminishing the segmentation quality.
