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.

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It's been a while since I've done this sort of article, but today I'm back, and I really think you're going to love this one. We're going to put our creative juices on hold and spend some quality time together exploring the history and evolution of those little critters that we like to call "icons". So, if you're into icon design as much as I am (digital fist bump while smiling like a crazy person), make a quick stop at the nearest espresso machine and grab a cup of that magical bean liquor, and then gently hop on back into the chair and let the journey begin. Well, I guess a lot of you already know the answer, but if the social sciences class "Research Methods and Techniques" taught me anything, it's that for each and every study (which this article clearly is), you should always start from the root level of your concept and then gradually build your way up using multiple layers of information. So, "icon" is a noun of Greek origin (eikόn), and is defined according to the Merriam Webster online dictionary as "a conventional religious image typically painted on a small wooden panel and used in the devotion of Eastern Christians."