A Semantic Space is Worth 256 Language Descriptions: Make Stronger Segmentation Models with Descriptive Properties

Abstract: 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 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.

• The paper introduces ProLab, which leverages language-generated descriptive properties to boost interpretability and performance in segmentation models.
• It employs K-Means to cluster these properties, resulting in higher accuracy across five benchmark datasets.
• The model demonstrates robust generalization by effectively segmenting unseen objects, mimicking human-like perception.

Innovating Semantic Segmentation with Property-Level Label Space

Semantic segmentation plays a crucial role in numerous practical applications, from autonomous driving to medical diagnosis. While traditional methods employ category-specific annotations, there's a new approach that offers a nuanced perspective to train segmentation models – ProLab introduces property-level label space, enabling more interpretable and stronger segmentation models.

Bridging the Gap Between Human Cognition and Machine Learning

In an exciting development, ProLab leverages LLMs to generate rich, descriptive properties about categories involved in segmentation tasks. These properties, clustering into interpretable sets through K-Means, resonate with human comprehension and reasoning. This innovative method allows segmentation models to perform with higher accuracy by recognizing the essential attributes and common sense knowledge corresponding to object categories.

Harnessing Descriptive Power for Robust Performance

The research showcases how ProLab outperforms traditional category-level supervision frameworks across five benchmark datasets, resulting in gains in model strength and scalability. With an eye on the future, the model's design embraces scalability, ensuring that as training steps extend, performance continues to enhance without saturation – a notable limitation of prior approaches.

ProLab's Generalization and Future Outlook

A standout feature of ProLab is its generalization capability. The model's foundational training on descriptive properties enables it to venture beyond known categories and segment never-seen-before objects, reflecting a deep understanding paralleling human perception. This remarkable ability positions ProLab as a transformational tool in the field, promising significant implications for future research and advancements in segmentation models and AI interpretation.