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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.16467 |
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| _version_ | 1866912206875000832 |
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| author | Hughes, Philip Burns, Larry Adams, Luke |
| author_facet | Hughes, Philip Burns, Larry Adams, Luke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16467 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation Hughes, Philip Burns, Larry Adams, Luke Computer Vision and Pattern Recognition 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. |
| title | Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.16467 |