Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation

• URL: http://arxiv.org/abs/2501.16467v1
• Date: Mon, 27 Jan 2025 20:02:12 GMT
• Title: Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation
• Authors: Philip Hughes, Larry Burns, Luke Adams,
• Abstract summary: LangSeg is a novel semantic segmentation method that leverages context-sensitive, fine-grained subclass descriptors. We evaluate LangSeg on two challenging datasets, ADE20K and COCO-Stuff, where it outperforms state-of-the-art models.
• Score: 0.0
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• Abstract: Semantic segmentation plays a crucial role in enabling machines to understand and interpret visual scenes at a pixel level. While traditional segmentation methods have achieved remarkable success, their generalization to diverse scenes and unseen object categories remains limited. Recent advancements in large language models (LLMs) offer a promising avenue for bridging visual and textual modalities, providing a deeper understanding of semantic relationships. In this paper, we propose LangSeg, a novel LLM-guided semantic segmentation method that leverages context-sensitive, fine-grained subclass descriptors generated by LLMs. Our framework integrates these descriptors with a pre-trained Vision Transformer (ViT) to achieve superior segmentation performance without extensive model retraining. We evaluate LangSeg on two challenging datasets, ADE20K and COCO-Stuff, where it outperforms state-of-the-art models, achieving up to a 6.1% improvement in mean Intersection over Union (mIoU). Additionally, we conduct a comprehensive ablation study and human evaluation to validate the effectiveness of our method in real-world scenarios. The results demonstrate that LangSeg not only excels in semantic understanding and contextual alignment but also provides a flexible and efficient framework for language-guided segmentation tasks. This approach opens up new possibilities for interactive and domain-specific segmentation applications.

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