Text-Driven Fusion for Infrared and Visible Images: Achieving Image Scene Adaptation on Hyperbolic Space

2026-06-13 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new text-driven fusion framework, TEDFusion, integrates infrared and visible images by employing hyperbolic manifold learning to overcome limitations of Euclidean methods that distort multi-modal interactions and semantic hierarchies. During training, BLIP-extracted text prompts act as topological anchors within hyperbolic space, guiding vision-attribute alignment through hyperbolic embeddings that naturally accommodate varying semantic granularities. Exploiting the Poincaré ball's negative curvature and exponential volume growth, this approach embeds hierarchical trees for coarse-to-fine semantics without metric saturation, while its vast peripheral space prevents texture distortion. At inference, the system autonomously adapts to input content using learned text-attribute priors, eliminating textual input. Experimental results demonstrate TEDFusion outperforms state-of-the-art approaches on benchmark datasets, with code available on GitHub.

Key takeaway

For Computer Vision Engineers developing multi-modal image fusion systems, this research suggests that adopting hyperbolic manifold learning can significantly improve results over traditional Euclidean methods. By leveraging hyperbolic space, your fusion models can better preserve semantic hierarchies and prevent texture distortion, leading to higher quality outputs. Consider exploring hyperbolic embeddings for your next infrared and visible image fusion project to achieve superior scene adaptation and detail retention.

Key insights

Hyperbolic manifold learning enables text-driven infrared and visible image fusion, preserving semantic hierarchies and preventing distortion.

Principles

• Hyperbolic space naturally models hierarchical data structures.
• Negative curvature prevents metric saturation in high-dimensional embeddings.
• Text prompts can guide cross-modal vision-attribute alignment.

Method

Train using BLIP-extracted text prompts as hyperbolic anchors to align vision attributes. Embed data into hyperbolic space, leveraging its geometry. At inference, adapt autonomously via learned text-attribute priors.

In practice

• Apply hyperbolic embeddings for multi-modal image fusion tasks.
• Utilize text prompts to guide unsupervised fusion adaptation.
• Improve semantic and texture preservation in fused images.

Topics

• Infrared and Visible Image Fusion
• Hyperbolic Manifold Learning
• Multi-modal Fusion
• Computer Vision
• Text-Driven Image Processing
• Poincaré Ball

Code references

• Shaoyun2023/TEDFusion

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.