Enhancing Vision Systems with Text-Guided Multimodal Fusion
Leveraging LLMs for RGB-Thermal fusion in challenging conditions
This research introduces a structurally simple yet adaptable multimodal fusion model that leverages large language models to combine RGB and thermal imaging for enhanced vision systems.
- Effectively combines visual data with thermal imaging for consistent performance across variable weather and lighting conditions
- Utilizes LLMs to extract valuable information from natural language prompts
- Creates a more adaptable and efficient approach compared to traditional complex fusion modules
- Particularly valuable for security applications including surveillance and autonomous driving systems that must operate reliably in challenging environmental conditions