Brief

Researchers have developed a new adapter module called Lighting Convolutional-Attention (LCA) to improve the robustness of foundation models like SAM for instance segmentation under varied lighting conditions. LCA processes RGB features alongside contrast maps to distinguish structural changes from illumination artifacts, enhancing segmentation accuracy without needing to fine-tune the entire model. The module is trained using a pairwise strategy with a specific loss term to penalize discrepancies between clean and illuminated images, and its effectiveness is validated on existing benchmarks and a new synthetic dataset designed for complex lighting. AI