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.
@article{arxiv.2203.12612,
title = {StructToken : Rethinking Semantic Segmentation with Structural Prior},
author = {Fangjian Lin and Zhanhao Liang and Sitong Wu and Junjun He and Kai Chen and Shengwei Tian},
journal= {arXiv preprint arXiv:2203.12612},
year = {2023}
}