Cross-Domain Semantic Segmentation with Large Language Model-Assisted Descriptor Generation
This addresses the challenge of improving segmentation performance across diverse scenes and unseen categories for applications in computer vision, though it is incremental as it builds on existing LLM and ViT methods.
The paper tackled the problem of limited generalization in semantic segmentation by proposing LangSeg, a method that uses large language models to generate descriptors and integrates them with a Vision Transformer, achieving up to a 6.1% improvement in mIoU on datasets like ADE20K and COCO-Stuff.
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.