  In previous deep-learning-based methods, semantic segmentation has been
regarded as a static or dynamic per-pixel classification task, \textit{i.e.,}
classify each pixel representation to a specific category. However, these
methods only focus on learning better pixel representations or classification
kernels while ignoring the structural information of objects, which is critical
to human decision-making mechanism. In this paper, we present a new paradigm
for semantic segmentation, named structure-aware extraction. Specifically, it
generates the segmentation results via the interactions between a set of
learned structure tokens and the image feature, which aims to progressively
extract the structural information of each category from the feature. Extensive
experiments show that our StructToken outperforms the state-of-the-art on three
widely-used benchmarks, including ADE20K, Cityscapes, and COCO-Stuff-10K.
