VesperMUSE: Multi-Modal Fusion Vision Transformer for Image Semantic Segmentation Under Diverse Light Conditions
VesperMUSE is a multi-modal framework designed to perform accurate semantic segmentation even in very low light conditions where images are noisy and distorted. It combines synthetic data generation, thermal image enhancement, and surface normal maps with a powerful Fusion Vision Transformer. The method achieves state-of-the-art results by intelligently fusing these different types of information to create robust scene understanding for critical applications like autonomous systems.