  Finetuning a pretrained backbone in the encoder part of an image transformer
network has been the traditional approach for the semantic segmentation task.
However, such an approach leaves out the semantic context that an image
provides during the encoding stage. This paper argues that incorporating
semantic information of the image into pretrained hierarchical
transformer-based backbones while finetuning improves the performance
considerably. To achieve this, we propose SeMask, a simple and effective
framework that incorporates semantic information into the encoder with the help
of a semantic attention operation. In addition, we use a lightweight semantic
decoder during training to provide supervision to the intermediate semantic
prior maps at every stage. Our experiments demonstrate that incorporating
semantic priors enhances the performance of the established hierarchical
encoders with a slight increase in the number of FLOPs. We provide empirical
proof by integrating SeMask into Swin Transformer and Mix Transformer backbones
as our encoder paired with different decoders. Our framework achieves a new
state-of-the-art of 58.25% mIoU on the ADE20K dataset and improvements of over
3% in the mIoU metric on the Cityscapes dataset. The code and checkpoints are
publicly available at
https://github.com/Picsart-AI-Research/SeMask-Segmentation .
