Multi-modal Video Representation Alignment for Robust Self-supervised Driver Distraction Detection
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
IMPACT Enhances robustness of AI systems in real-world multi-modal video understanding tasks like driver safety.