论文标题
结构性：通过结构性先验重新思考语义细分
StructToken : Rethinking Semantic Segmentation with Structural Prior
论文作者
论文摘要
在以前的基于深度学习的方法中，语义分割被认为是静态或动态的每个像素分类任务，\ textit {i.e。，}将每个像素表示分类为特定类别。但是，这些方法仅着眼于学习更好的像素表示或分类内核，而忽略对象的结构信息，这对于人类决策机制至关重要。在本文中，我们提出了一种用于语义分割的新范式，称为结构感知的提取。具体而言，它通过一组学习的结构令牌与图像特征之间的相互作用生成分割结果，该功能旨在从该功能中逐步提取每个类别的结构信息。广泛的实验表明，我们的结构量优于三个广泛使用的基准，包括ADE20K，CityScapes和Coco-Stuff-10k。
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.
