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.

本文提出了一种新的语义分割范式，名为结构感知抽取，并通过一组学习到的结构单元与图像特征之间的交互逐步提取每个类别的结构信息来生成分割结果，实验证明其优于目前广泛使用的三个基准测试。