  The fully-convolutional network (FCN) with an encoder-decoder architecture
has become the standard paradigm for semantic segmentation. The encoder-decoder
architecture utilizes an encoder to capture multi-level feature maps, which are
then incorporated into the final prediction by a decoder. As the context is
critical for precise segmentation, tremendous effort has been made to extract
such information in an intelligent manner, including employing dilated/atrous
convolutions or inserting attention modules. However, the aforementioned
endeavors are all based on the FCN architecture with ResNet backbone which
cannot tackle the context issue from the root. By contrast, we introduce the
Swin Transformer as the backbone to fully extract the context information and
design a novel decoder named densely connected feature aggregation module
(DCFAM) to restore the resolution and generate the segmentation map. The
extensive experiments on two datasets demonstrate the effectiveness of the
proposed scheme.
