Brief

Researchers have introduced FusionRS, a novel large-scale dataset designed to advance dual-modal vision-language foundation models in remote sensing. This dataset uniquely combines RGB and infrared imagery with corresponding text captions, addressing the under-exploration of infrared data in current models. Experiments using FusionRS demonstrate improved performance in tasks like RGB-IR alignment and captioning, highlighting the value of modality-specific textual supervision. AI

Researchers have developed a new framework for multi-modal video representation alignment to improve self-supervised learning for driver distraction detection. This approach addresses challenges with noisy or faulty data from multiple sensors by jointly modeling unreliable positives and negatives. The method uses soft targets and a similarity-based weighting mechanism to achieve principled global multi-modal alignment, outperforming existing baselines on the Drive&Act dataset. AI