This paper introduces ProLab, a novel approach using property-level label space for creating strong interpretable segmentation models. Instead of relying solely on category-specific annotations, ProLab uses descriptive properties grounded in common sense knowledge for supervising segmentation models. It is based on two core designs. First, we employ Large Language Models (LLMs) and carefully crafted prompts to generate descriptions of all involved categories that carry meaningful common sense knowledge and follow a structured format. Second, we introduce a description embedding model preserving semantic correlation across descriptions and then cluster them into a set of descriptive properties (e.g., 256) using K-Means. These properties are based on interpretable common sense knowledge consistent with theories of human recognition. We empirically show that our approach makes segmentation models perform stronger on five classic benchmarks (e.g., ADE20K, COCO-Stuff, Pascal Context, Cityscapes, and BDD). Our method also shows better scalability with extended training steps than category-level supervision. Our interpretable segmentation framework also emerges with the generalization ability to segment out-of-domain or unknown categories using only in-domain descriptive properties. Code is available at https://github.com/lambert-x/ProLab.

ProLab是一种创新的方法，通过使用基于属性级标签空间来创建强大且可解释的分割模型。它基于两个核心设计：一是使用大型语言模型和精心设计的提示生成具有意义的常识知识的所有类别的描述，二是引入描述嵌入模型以保持描述之间的语义相关性，并将它们聚类为一组描述性属性，这些属性基于可解释的与人类识别理论一致的常识知识。实验证明，我们的方法使分割模型在五个经典基准测试上表现更强大，并且与类别级监督相比具有更好的可扩展性。我们的可解释分割框架还具备利用领域内描述性属性仅对领域外或未知类别进行分割的泛化能力。

一个语义空间足以呈现256种语言描述：利用描述性属性增强分割模型