3 accepted papers
Bridging the modality gap between infrared and visible imagery is critical for cross-modal understanding and for enriching multimodal benchmarks. However, existing approaches remain confined to one-to-one mappings and are typically evaluated on unidirectional or closed-set scenarios. To address this
A major obstacle for high-level tasks is the unpredictable image degradation. While several architectures proposed to address this, they fail under extreme degradation. Therefore, we introduce a novel cross-modality pipeline called AIRMamba, designed to holistically and robustly restore images degra…
The infrared image can distinguish the targets from the background based on radiation differences, providing more significant target visibility under dense haze. Fusion of visible haze images with infrared prior representations can generate high-quality fused images for high-level tasks. Consequentl…